Alternatives to Alchemite
Compare Alchemite alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Alchemite in 2026. Compare features, ratings, user reviews, pricing, and more from Alchemite competitors and alternatives in order to make an informed decision for your business.
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GPT-Rosalind
OpenAI
GPT-Rosalind is a purpose-built frontier reasoning model developed by OpenAI to accelerate scientific research across biology, drug discovery, and translational medicine. It is designed specifically for life sciences workflows, where researchers must navigate large volumes of literature, experimental data, and specialized databases to generate and validate new ideas. It combines deep domain understanding in areas such as chemistry, genomics, protein engineering, and disease biology with advanced tool-use capabilities, allowing it to interact with scientific databases, analyze experimental outputs, and support complex, multi-step reasoning tasks. It can assist with evidence synthesis, hypothesis generation, literature review, sequence interpretation, and experimental planning, helping scientists move faster from raw data to actionable insights. GPT-Rosalind transforms complex, time-intensive research processes into more efficient AI-assisted workflows. -
2
NVIDIA PhysicsNeMo
NVIDIA
NVIDIA PhysicsNeMo is an open source Python deep-learning framework for building, training, fine-tuning, and inferring physics-AI models that combine physics knowledge with data to accelerate simulations, create high-fidelity surrogate models, and enable near-real-time predictions across domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather and climate, and digital twin applications. It provides scalable, GPU-accelerated tools and Python APIs built on PyTorch and released under the Apache 2.0 license, offering curated model architectures including physics-informed neural networks, neural operators, graph neural networks, and generative AI–based approaches so developers can harness physics-driven causality alongside observed data for engineering-grade modeling. PhysicsNeMo includes end-to-end training pipelines from geometry ingestion to differential equations, reference application recipes to jump-start workflows.Starting Price: Free -
3
StarDrop
Optibrium
With its comprehensive suite of integrated software, StarDrop™ delivers best-in-class in silico technologies within a highly visual and user-friendly interface. StarDrop™ enables a seamless flow from the latest data through predictive modeling to decision-making regarding the next round of synthesis and research, improving the speed, efficiency, and productivity of the discovery process. Successful compounds require a balance of many different properties. StarDrop™ guides you through this multi-parameter optimization challenge to target compounds with the best chance of success, saving you time and resources by enabling you to synthesize and test fewer compounds. -
4
L7|ESP
L7 Informatics
L7 Enterprise Science Platform (L7|ESP®) is a unified platform that contextualizes data and eliminates business silos via process orchestration. It's a comprehensive solution that facilitates the digitalization of data and scientific processes in life sciences organizations. L7|ESP has native applications, including L7 LIMS, L7 Notebooks, L7 MES, L7 Scheduling, and more. It can integrate with existing third-party applications, lab instruments, and devices to capture all data in a single data model. It has a low-code/no-code workflow designer and hundreds of pre-built connectors to enable rapid time-to-value and end-to-end automation. By leveraging a single data model, L7|ESP enables advanced bioinformatics, AI, and ML to offer novel scientific and operational insights. L7|ESP addresses data and lab management needs in life sciences, particularly in: ● Research and Diagnostics ● Pharma and CDMO ● Clinical Sample Management Resource Center: l7informatics dot com/resource-center -
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BenevolentAI
BenevolentAI
BenevolentAI is an AI-enabled drug discovery platform and scientific technology company that unites advanced artificial intelligence, machine learning, and domain-specific science to accelerate the discovery, design, and development of new medicines for complex diseases by making sense of vast, diverse biomedical data and generating actionable scientific insights faster than traditional methods. Its proprietary Benevolent Platform ingests and harmonizes structured and unstructured biomedical information, including literature, genomics, clinical information, and multi-omics data, into a comprehensive knowledge graph, enabling scientists to reason across biological systems, generate hypotheses, predict novel drug targets, and design candidate molecules with higher confidence and lower failure rates. -
6
AQBioSim
SandboxAQ
AQBioSim is a cloud-native platform developed by SandboxAQ that leverages Large Quantitative Models (LQMs) grounded in physics and chemistry to revolutionize materials discovery and optimization. By integrating Density Functional Theory (DFT), Iterative Full Configuration Interaction (iFCI), Generative AI, Bayesian Optimization, and Chemical Foundation Models, AQBioSim enables high-fidelity simulations of molecular and material behaviors under real-world conditions. AQBioSim's capabilities include predicting performance under various stresses, accelerating formulation through in silico testing, and exploring sustainable chemical processes. Notably, AQBioSim has demonstrated significant advancements in battery technology by reducing lithium-ion battery end-of-life prediction time by 95%, achieving 35x greater accuracy with 50x less data. -
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Scitara DLX
Scitara
Scitara DLX™ offers a rapid connectivity infrastructure for any instrument in the life science laboratory in a fully compliant and auditable cloud-based platform. Scitara DLX™ is a universal digital data infrastructure that connects any instrument, resource, app and software in the laboratory. The cloud-based, fully auditable platform connects all data sources across the lab, allowing the free flow of data across multiple end points. This allows scientists to devote their time to scientific research, not waste it solving data issues. DLX curates and corrects data in flight to support the development of accurate, properly structured data models that feed AI and ML systems. This supports a successful digital transformation strategy in the pharma and biopharma industries. Unlocking insights from scientific data enables faster decision-making in drug discovery and development, helping bring drugs to market more quickly. -
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BIOVIA Discovery Studio
Dassault Systèmes
Today’s biopharmaceutical industry is marked by complexity: growing market demands for improved specificity and safety, novel treatment classes and more intricate mechanisms of disease. Keeping up with this complexity requires a deeper understanding of therapeutic behavior. Modeling and simulation methods provide a unique means to explore biological and physicochemical processes down to the atomic level. This can guide physical experimentation, accelerating the discovery and development process. BIOVIA Discovery Studio brings together over 30 years of peer-reviewed research and world-class in silico techniques such as molecular mechanics, free energy calculations, biotherapeutics developability and more into a common environment. It provides researchers with a complete toolset to explore the nuances of protein chemistry and catalyze discovery of small and large molecule therapeutics from Target ID to Lead Optimization. -
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LiveDesign
Schrödinger
LiveDesign is an enterprise informatics platform that enables teams to rapidly advance drug discovery projects by collaborating, designing, experimenting, analyzing, tracking, and reporting in a centralized platform. Capture ideas alongside experimental and modeling data. Create and store new virtual compounds in a centralized database, evaluate through advanced models, and prioritize new designs. Integrate biological data and model results across federated corporate databases, apply sophisticated cheminformatics to analyze all data at once, and progress compounds faster. Drive predictions and designs using advanced physics-based methods paired with machine learning techniques to rapidly improve prediction accuracy. Work together with remote team members in real-time. Share ideas, test, revise, and advance chemical series without losing track of your work. -
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Edison Analysis
Edison Scientific
Edison Analysis is a next-generation scientific data-analysis agent built by Edison Scientific. It is the analytical engine underpinning their AI Scientist platform, Kosmos, and it’s available both on Edison’s platform and via API. Edison Analysis performs complex scientific data analysis by iteratively building and updating Jupyter notebooks in a dedicated environment; given a dataset plus a prompt, the agent explores, analyzes, and interprets the data to provide comprehensive insights, reports, and visualizations, very much like a human scientist. It supports execution of Python, R, and Bash code, and includes a full suite of common scientific-analysis packages in a Docker environment. Because all work is done within a notebook, the reasoning is fully transparent and auditable; users can inspect exactly how data was manipulated, which parameters were chosen, how conclusions were drawn, and can download the notebook and associated assets at any time.Starting Price: $50 per month -
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Sapio Sciences
Sapio Sciences
Sapio Sciences delivers the Sapio Platform, an agentic AI lab informatics platform that makes life in the lab easier and more productive for scientists. The unified, configurable, low code and scalable environment brings together Sapio LIMS, the market’s most advanced and flexible LIMS for automating research, diagnostics and manufacturing, Sapio ELaiN, the third generation AI lab notebook and scientific co scientist, and Sapio Scientific Data Cloud, the scientific data unification solution with built in organization, search, charting, tools and AI. Biopharma R&D, biotech, CRO and clinical diagnostics organizations use Sapio to run complex workflows and keep samples, experiments and data connected in one place instead of juggling disconnected systems. -
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NVIDIA BioNeMo
NVIDIA
BioNeMo is an AI-powered drug discovery cloud service and framework built on NVIDIA NeMo Megatron for training and deploying large biomolecular transformer AI models at a supercomputing scale. The service includes pre-trained large language models (LLMs) and native support for common file formats for proteins, DNA, RNA, and chemistry, providing data loaders for SMILES for molecular structures and FASTA for amino acid and nucleotide sequences. The BioNeMo framework will also be available for download for running on your own infrastructure. ESM-1, based on Meta AI’s state-of-the-art ESM-1b, and ProtT5 are transformer-based protein language models that can be used to generate learned embeddings for tasks like protein structure and property prediction. OpenFold, a deep learning model for 3D structure prediction of novel protein sequences, will be available in BioNeMo service. -
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Evo 2
Arc Institute
Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins. It utilizes a frontier deep learning architecture to model biological sequences at single-nucleotide resolution, achieving near-linear scaling of compute and memory relative to context length. Trained with 40 billion parameters and a 1 megabase context length, Evo 2 processes over 9 trillion nucleotides from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function prediction across multiple biological modalities, including DNA, RNA, and proteins, and to generate novel sequences with plausible genomic architecture. The model's capabilities have been demonstrated in tasks such as designing functional CRISPR systems and predicting disease-causing mutations in human genes. Evo 2 is publicly accessible via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework. -
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NVIDIA Modulus
NVIDIA
NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly. -
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Recursion
Recursion
Recursion is a TechBio company focused on transforming drug discovery by combining biology, data, and artificial intelligence. Founded over a decade ago, the company pioneered the use of large-scale cellular imaging to train AI models that decode the biological drivers of disease. Recursion’s mission is to deliver better medicines through novel insights and precision design, reducing the high failure rates of traditional drug development. Its proprietary Recursion OS platform integrates massive biological datasets with machine learning to accelerate discovery from target identification to clinical development. The company has built an advanced pipeline of potential first-in-class and best-in-class therapies targeting aggressive cancers and rare diseases. Automated wet labs and robotics enable millions of experiments per week, feeding continuous learning into its AI models. -
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Schrödinger
Schrödinger
Transform drug discovery and materials research with advanced molecular modeling. Our physics-based computational platform integrates differentiated solutions for predictive modeling, data analytics, and collaboration to enable rapid exploration of chemical space. Our platform is deployed by industry leaders worldwide for drug discovery, as well as for materials science in fields as diverse as aerospace, energy, semiconductors, and electronics displays. The platform powers our own drug discovery efforts, from target identification to hit discovery to lead optimization. It also drives our research collaborations to develop novel medicines for critical public health needs. With more than 150 Ph.D. scientists on our team, we invest heavily in R&D. We’ve published over 400 peer-reviewed papers that demonstrate the strength of our physics-based approaches, and we’re continually pushing the limits of computer modeling. -
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Digimat
e-Xstream engineering
e-Xstream engineering develops and commercializes the Digimat suite of software, a state-of-the-art multi-scale material modeling technology that speeds up the development process for composite materials and structures. Digimat is a core technology of 10xICME Solution and is used to perform detailed analyses of materials on the microscopic level and to derive micromechanical material models suited for multi-scale coupling of the micro- and macroscopic level. Digimat material models provide the means to combine processing simulation with structural FEA. This means to move towards more predictive simulation by taking into account the influence of processing conditions on the performance of the finally produced part. As an efficient and predictive tool Digimat helps its users to design and manufacture innovative composite materials and parts with great efficiency in time and costs. -
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BioSymetrics
BioSymetrics
We integrate clinical and experimental data using machine learning to navigate human disease biology and advance precision medicines. Our patent-pending Contingent AI™ understands relationships within the data to provide sophisticated insights. We address data bias by iterating on machine learning models based upon decisions made in the pre-processing and feature engineering stages. We leverage zebrafish, cellular and other phenotypic animal models to validate in silico predictions in vivo experiments and genetically modify them in vitro and in vivo, to improve translation. Using active learning and computer vision on validated models for cardiac, central nervous system and rare disorders, we rapidly incorporate new data into our machine learning models. -
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Geminus
Geminus
Geminus unleashes the power of predictive intelligence by intersecting AI and physics with multi-fidelity modeling. Our novel, first-principles AI translates the constraints of the physical world inside resilient predictive models. The Geminus platform leverages sparse data to quickly analyze the behavior of complex industrial systems, and precisely predict the impact of decisions that drive your business forward. The Geminus multi-fidelity approach fuses models with data, which enables you to create highly accurate surrogates over 1,000x faster than simulation. Only Geminus accurately quantifies model uncertainty, so you can be confident in your predictions and the decisions they inspire. Geminus compresses model creation time from months to hours requiring far fewer data and computes resources than traditional AI, or simulation methods. Models built on Geminus are infused with an understanding of the known behavior of real-world systems. -
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Eidogen-Sertanty Target Informatics Platform (TIP)
Eidogen-Sertanty
Eidogen-Sertanty's Target Informatics Platform (TIP) is the world's first structural informatics system and knowledgebase that enables researchers with the ability to interrogate the druggable genome from a structural perspective. TIP amplifies the rapidly expanding body of experimental protein structure information and transforms structure-based drug discovery from a low-throughput, data-scarce discipline into a high-throughput, data-rich science. Designed to help bridge the knowledge gap between bioinformatics and cheminformatics, TIP supplies drug discovery researchers with a knowledge base of information that is both distinct from and highly complementary to information furnished by existing bio- and cheminformatics platforms. TIP's seamless integration of structural data management technology with unique target-to-lead calculation and analysis capabilities enhances all stages of the discovery pipeline. -
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Benchling
Benchling
Legacy R&D software is a drain on scientific potential. It slows down R&D progress, scatters data across silos, and wipes out institutional knowledge. Benchling is the industry’s leading life sciences R&D cloud. Accelerate, measure, and forecast R&D – from discovery through bioprocessing – all in one place. A suite of seven natively unified applications that accelerate R&D at all levels. Codeless configuration, open integration, and dashboards tailored to your needs. Deep life science R&D and consulting expertise ensure ongoing success. Benchling is a unified R&D platform, so you spend less time entering and hunting for data, and more time working together to move your research forward. Scientists, managers, and executives can optimize R&D output with complete visibility into experimental context, program performance, and resource utilization. -
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BenchSci
BenchSci
Eliminate inefficiencies and errors in the entire reagent and model system selection process that cause costly experimental failure. Accelerate projects by selecting reagents and model systems in 30 seconds vs. 12 weeks. Reduce hard cost of consumables and save millions per year. Empower organizational purpose by restoring research time to scientists. See real business impact from AI with a proven, turnkey application. Over 41,200 scientists in 15 of the top 20 pharmaceutical companies and more than 4,450 academic institutions use BenchSci’s AI-Assisted Antibody Selection to plan more successful experiments, with proven savings of millions per year in hard costs alone. But antibodies constitute just 40-50% of reagent failures. Get comprehensive experimental evidence, reagent and model system catalog data, and independent validations within a single intuitive interface. Real world experiment data from 11.2 million scientific publications, including closed-access papers. -
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NVIDIA Clara
NVIDIA
Clara’s domain-specific tools, AI pre-trained models, and accelerated applications are enabling AI breakthroughs in numerous fields, including medical devices, imaging, drug discovery, and genomics. Explore the end-to-end pipeline of medical device development and deployment with the Holoscan platform. Build containerized AI apps with the Holoscan SDK and MONAI, and streamline deployment in next-generation AI devices with the NVIDIA IGX developer kits. The NVIDIA Holoscan SDK includes healthcare-specific acceleration libraries, pre-trained AI models, and reference applications for computational medical devices. -
24
FutureHouse
FutureHouse
FutureHouse is a nonprofit AI research lab focused on automating scientific discovery in biology and other complex sciences. FutureHouse features superintelligent AI agents designed to assist scientists in accelerating research processes. It is optimized for retrieving and summarizing information from scientific literature, achieving state-of-the-art performance on benchmarks like RAG-QA Arena's science benchmark. It employs an agentic approach, allowing for iterative query expansion, LLM re-ranking, contextual summarization, and document citation traversal to enhance retrieval accuracy. FutureHouse also offers a framework for training language agents on challenging scientific tasks, enabling agents to perform tasks such as protein engineering, literature summarization, and molecular cloning. Their LAB-Bench benchmark evaluates language models on biology research tasks, including information extraction, database retrieval, etc. -
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Nygen
Nygen
Nygen is a cloud-based single-cell RNA-seq (scRNA-seq) and multi-omics data analysis and discovery platform designed to let researchers upload, explore, visualize, analyze and interpret complex cellular datasets with an intuitive, no-code interface that supports drag-and-drop workflows and advanced scientific analysis without requiring programming expertise; it combines Nygen Analytics for rapid, reproducible scRNA-seq exploration with collaborative dashboards and publication-ready outputs, Nygen Database for accessing and hosting curated single-cell datasets to accelerate research and comparative studies, and Nygen Insights, an AI-augmented tool that delivers highly accurate cell annotations, in-depth disease impact analysis and tailored biological insights; it supports a wide range of data formats, integrates public data, enables secure cloud-based collaboration, and provides features like literature-linked evidence and biomarker-focused analyses. -
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AQChemSim
SandboxAQ
AQChemSim is a cloud-native platform developed by SandboxAQ that leverages Large Quantitative Models (LQMs) grounded in physics and chemistry to revolutionize materials discovery and optimization. By integrating Density Functional Theory (DFT), Iterative Full Configuration Interaction (iFCI), Generative AI, Bayesian Optimization, and Chemical Foundation Models, AQChemSim enables high-fidelity simulations of molecular and material behaviors under real-world conditions. AQChemSim's capabilities include predicting performance under various stresses, accelerating formulation through in silico testing, and exploring sustainable chemical processes. Notably, AQChemSim has demonstrated significant advancements in battery technology by reducing lithium-ion battery end-of-life prediction time by 95%, achieving 35x greater accuracy with 50x less data. -
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Dotmatics
Dotmatics
Dotmatics is the global leader in R&D scientific software that connects science, data, and decision-making. Combining a workflow and data platform with best-of-breed applications, we offer the first true end-to-end solutions for biology, chemistry, formulations, data management, flow cytometry, and more. Trusted by more than 2 million researchers from the world’s leading biopharma, chemicals and materials enterprises, and academic institutions, we are dedicated to working with the scientific community to help make the world a healthier, cleaner and safer place to live. Learn more about our platform and products, including GraphPad Prism, Geneious, SnapGene, Protein Metrics, LabArchives, and more. -
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Causaly
Causaly
Leverage the power of AI to expedite the journey from bench research and laboratory insights to the launch of life-changing therapies. Gain up to 90% in research productivity by reducing your reading time from months to minutes. Cut through the noise with a high-precision, high-accuracy search to navigate the ever-growing volume of scientific literature with ease. Save time, reduce bias and increase odds of novel discoveries. Deeply explore disease biology and conduct advanced target discovery. Causaly’s high-precision knowledge graph consolidates evidence from millions of publications, making deep, unbiased scientific exploration possible. Rapidly navigate biological cause-and-effect relationships without being an expert. Get a view of all scientific documents and uncover hidden connections. Causaly’s powerful AI machine reads millions of published biomedical literature to support better decision-making and research outcomes. -
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Axiomatic AI
Axiomatic AI
Axiomatic AI is an advanced artificial intelligence platform designed to accelerate scientific research and engineering workflows by combining generative AI with mathematical verification and physics-based reasoning. It is built around a concept called Axiomatic Intelligence, which integrates frontier AI models with formal logic and domain-specific world models to ensure that outputs are not only generated but also mathematically and physically validated. Unlike conventional AI systems that produce plausible answers without guarantees of correctness, Axiomatic AI uses verification systems that test results against formal specifications and engineering constraints before returning them to the user. This approach allows the platform to support mission-critical tasks in areas such as photonics, electronics, thermal engineering, mechanics, and signal analysis. -
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Khimera
Kintech Laboratory
Khimera is used to calculate the kinetic parameters of microscopic processes, thermodynamic and transport properties of substances and their mixtures in gases, plasmas and gas-solid phases boundary. The primary users are researchers and engineers, involved in kinetic model development as well as thermodynamic and kinetic modeling for chemical engineering, combustion, catalysis, metallurgy, and microelectronics areas. Khimera ideally fits the needs of multi-scale modeling providing the link between fundamental molecular properties of individual molecules and mesoscale ensemble-averaged characteristics of the reactive medium: thermodynamic and transport properties as well as rates of chemical reactions. All the models can use the results of quantum-chemical simulations as an input, thus providing the possibility to recover properties without any experimental input from the user side. -
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Chemaxon Design Hub
Chemaxon
A platform that connects scientific rationale, compound design, and computational resources. Chemaxon’s Design Hub for medicinal chemistry from analysis to prioritize ideas. Design Compounds and manage ideas within one platform. A single platform that connects scientific rationale, compound design, and computational resources. Switch from PowerPoint files to graphical and chemically searchable hypotheses that are an integral part of the compound design process. Easily work with your trusted phys-chem properties, computational models, novelty issues, or purchasable compound catalogs in a rich visual environment. Involve your CROs in the compound progression process using this secure online service. Analyze collected evidence from biological assays or experimental structural information, extract SAR, and make new hypotheses for the next optimization iteration. Store your scientific hypotheses in a “designer's ELN” (chemically aware drawing canvases). -
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AlphaFold
DeepMind
These exquisite, intricate machines are proteins. They underpin not just the biological processes in your body but every biological process in every living thing. They’re the building blocks of life. Currently, there are around 100 million known distinct proteins, with many more found every year. Each one has a unique 3D shape that determines how it works and what it does. But figuring out the exact structure of a protein remains an expensive and often time-consuming process, meaning we only know the exact 3D structure of a tiny fraction of the proteins known to science. Finding a way to close this rapidly expanding gap and predict the structure of millions of unknown proteins could not only help us tackle disease and more quickly find new medicines but perhaps also unlock the mysteries of how life itself works. -
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Reliant AI
Reliant AI
Accuracy, speed, confidence. Introducing generative AI to commercial biopharma. Simplify the labor-intensive process of collecting, organizing, and inspecting vast amounts of complex data. Get straight to decision-critical insights with 100% confidence, every time. With our AI-powered data manipulation and verification platform, you'll never lose track of your workstreams again. Gather, refine, and check your data, all in one place. Search public and private databases by key drug characteristics. Segment drugs and trials by detailed patient profiles. Extract the data you need in plain English. Support your findings by linking answers back to their source. Focus your time and energy on synthesizing high-quality outputs from data rather than menially sifting through it. Our specialized LLMs enable researchers to perform asset scans 4.8x faster than by hand. We index over 38M scientific publications, conference abstracts, and clinical trials. All the data you need, when you need it. -
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JADBio AutoML
JADBio
JADBio is a state-of-the-art automated Machine Learning Platform without the need for coding. With its breakthrough algorithms it can solve open problems in machine learning. Anybody can use it and perform a sophisticated and correct machine learning analysis even if they do not know any math, statistics, or coding. It is purpose-built for life science data and particularly molecular data. This means that it can deal with the idiosyncrasies of molecular data such as very low sample size and very high number of measured quantities that could reach to millions. Life scientists need it to understand what are the features and biomarkers that are predictive and important, what is their role, and get intuition about the molecular mechanisms involved. Knowledge discovery is often more important than a predictive model. So, JADBio focuses on feature selection and its interpretation.Starting Price: Free -
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BIOVIA COSMO-RS
Dassault Systèmes
BIOVIA COSMO-RS is a comprehensive toolbox for modeling and predicting fluid phase properties, enabling chemical engineers, chemists, formulation engineers, and materials scientists to research and develop new solutions faster and more efficiently than with test and experimentation alone, thus accelerating innovation and reducing costs. COSMO-RS simulations are based on a sound scientific theory, which ensures robust and reliable predictions over the whole range of chemistry in the liquid state. The first-principle approach allows for predictions of new, not yet synthesized compounds, reaching beyond the known chemical space. BIOVIA’s COSMO team consists of the original inventors of COSMO-RS, assuring timely support and prime expertise to help solve even the most challenging problems in solution thermodynamics. Key benefits include a robust scientific foundation combining quantum chemistry and thermodynamics to ensure accuracy and reliability. -
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alvaBuilder
Alvascience
alvaBuilder is a no-code de novo molecular design software for generating novel chemical structures that satisfy user-defined structural, physicochemical, and modeling constraints. It enables the creation of new molecules starting from scratch or by evolving existing structures using fragment-based and rule-driven approaches. alvaBuilder integrates seamlessly with QSAR/QSPR workflows, allowing users to guide molecule generation using predictive models, descriptor ranges, and property targets. The software supports medicinal chemistry, lead optimization, and virtual screening tasks by efficiently exploring chemical space while maintaining chemical feasibility and interpretability. alvaBuilder is designed for research and industrial applications where transparent, controllable, and reproducible molecular generation is required. -
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ChemCopilot
ChemCopilot
ChemCopilot is an AI-native chemical formulation and product lifecycle management platform designed to transform how scientists, engineers, and R&D teams design, test, optimize, and manage chemical products and processes by combining advanced artificial intelligence with domain-specific chemistry knowledge, regulatory data, simulation capabilities, and real-time insights. It automates validation of product labels, ingredient restrictions, and safety data sheets against global compliance frameworks, eliminating disconnected spreadsheets and manual review while providing audit trails and real-time alerts to support regulatory adherence. ChemCopilot accelerates innovation by simulating chemical reactions, molecular interactions, and process workflows to predict formulation performance and outcomes that traditional general-purpose tools cannot provide, and it integrates real-time data from laboratory and industrial systems to drive data-driven decisions. -
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alvaModel
Alvascience
alvaModel is a software tool for building, validating, comparing, and applying QSAR and QSPR models. It supports regression and classification workflows based on molecular descriptors and fingerprints, with a strong focus on model transparency, interpretability, and scientific robustness. The software includes multiple data splitting strategies, variable selection methods, modeling algorithms, and comprehensive internal and external validation procedures. alvaModel provides diagnostic plots, applicability domain analysis, and model comparison tools to support the identification of reliable and predictive models. Designed according to best practices in chemometrics, alvaModel facilitates the development of interpretable models consistent with the OECD principles for QSAR validation, making it suitable for research and regulatory-oriented applications. The graphical interface guides users through the entire modeling workflow while allowing full control over each modeling step. -
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Metabolon
Metabolon
At Metabolon, we offer the largest Level 1 library in the metabolomics industry. Our proprietary library has been built and curated over 20 years and contains over 5,400 entries. The vast majority of entries in our library are Level 1 attributing approximately 85% (~4,600 entries); however, some are Level 2 (approximately 15% accounting for around 800 entries) due to a lack of commercial standards available to qualify for Level 1. Metabolon delivers accurate, highly actionable insights for our clients’ scientific or clinical inquiries due to our unmatched library breadth and industry-leading annotation confidence levels. Metabolomics applies to a wide range of research, from soil health to food nutrition and preclinical research to clinical trials. Whether you’re searching for trends in a group or refining an individual’s treatment, metabolomics can help you find answers to important questions. -
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Elucidata Polly
Elucidata
Harness the power of biomedical data with Polly. The Polly Platform helps to scale batch jobs, workflows, coding environments and visualization applications. Polly allows resource pooling and provides optimal resource allocation based on your usage requirements and makes use of spot instances whenever possible. All this leads to optimization, efficiency, faster response time and lower costs for the resources. Get access to a dashboard to monitor resource usage and cost real time and minimize overhead of resource management by your IT team. Version control is integral to Polly’s infrastructure. Polly ensures version control for your workflows and analyses through a combination of dockers and interactive notebooks. We have built a mechanism that allows the data, code and the environment co-exist. This coupled with data storage on the cloud and the ability to share projects ensures reproducibility of every analysis you perform. -
41
OmicsBox
BioBam Bioinformatics S.L.
OmicsBox is a leading bioinformatics solution that offers end-to-end data analysis of genomes, transcriptomes, metagenomes, and genetic variation studies. The application is used by top private and public research institutions worldwide and allows researchers to easily process large and complex data sets, and streamline their analysis process. It is designed to be user-friendly, efficient, and with a powerful set of tools to extract biological insights from omics data. The software is structured in different modules, each with a specific set of tools and functions designed to perform different types of analysis, such as de-novo genome assemblies, genetic variation analysis, differential expression analysis, and taxonomic classifications of microbiome data, including the functional interpretation and rich visualizations of results. The functional analysis module includes the popular Blast2GO annotation methodology and makes OmicsBox particularly suited for non-model organism researchStarting Price: €100/month/seat -
42
Aurora Drug Discovery
Aurora Fine Chemicals
Aurora employs quantum mechanics, thermodynamics, and an advanced continuous water model for solvation effects to calculate ligand´s binding affinities. This approach differs dramatically from scoring functions that are commonly used for binding affinity predictions. By including the entropy and aqueous electrostatic contributions in to the calculations directly, Aurora algorithms produce much more accurate and robust values of binding free energies. Interaction of a ligand with a protein is characterized by the value of binding free energy. The free energy (F) is the thermodynamic quantity that is directly related to experimentally measurable value of inhibition constant (IC50) and depends on electrostatic, quantum, aqueous solvation forces, as well as on statistical properties of interacting molecules. There are two major contributing quantities leading to non-additivity in F: 1) the electrostatic and solvation energy and 2) the entropy. -
43
Cytel
Cytel
Cytel is a leading global provider of clinical trial design software, biometric services, and advanced analytics, specializing in optimizing clinical trials and assisting pharmaceutical companies in unlocking the full potential of their clinical and real-world data. Founded in 1987 by distinguished statisticians Cyrus Mehta and Nitin Patel, Cytel has been at the forefront of adaptive clinical trial technology and biostatistical science. Our software solutions, including the East Horizon platform, empower precise trial design and simulation, utilizing adaptive and Bayesian tools to optimize protocols and accelerate drug development. The East Horizon platform integrates key components of Cytel's trusted software portfolio into a unified solution with R integration, enhancing trial design capabilities. Additionally, Cytel offers the Xact software suite, a comprehensive toolkit for statistical analyses of small datasets, and sparse, and missing data. -
44
Microsoft Discovery
Microsoft
Microsoft Discovery is a new agentic platform designed to revolutionize research and development (R&D) by empowering scientists and engineers with AI-driven collaboration and high-performance computing (HPC). Built on Azure, this platform enables researchers to work alongside specialized AI agents that help accelerate the discovery process through advanced knowledge reasoning, hypothesis formulation, and experimental simulations. The platform's graph-based knowledge engine facilitates complex, contextual reasoning over vast amounts of scientific data, promoting transparency and accountability while speeding up the discovery cycle. By automating and enhancing research tasks, Microsoft Discovery offers an extensible, enterprise-ready solution that integrates seamlessly with existing tools and datasets. -
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Materials Zone
Materials Zone
From materials data to better products, faster! Accelerates R&D, scale-up, and optimizes manufacturing QC and supply chain decisions. Discover new materials, use ML guidance to forecast outcomes, and achieve faster and improved results. Build a model on your way to production. Test the model's limits behind your products to design cost-efficient and robust production lines. Use models to predict future failures based on supplied materials informatics and production line parameters. The Materials Zone platform aggregates data from independent entities, materials providers, factories, or manufacturing facilities, communicating between them through a secured platform. By using machine learning (ML) algorithms on your experimental data, you can discover new materials with desired properties, generate ‘recipes’ for materials synthesis, build tools to analyze unique measurements automatically, and retrieve insights. -
46
MPCPy
MPCPy
MPCPy is a Python package that facilitates the testing and implementation of occupant-integrated model predictive control (MPC) for building systems. The package focuses on the use of data-driven, simplified physical or statistical models to predict building performance and optimize control. Four main modules contain object classes to import data, interact with real or emulated systems, estimate and validate data-driven models, and optimize control input. While MPCPy provides an integration platform, it relies on free, open-source, third-party software packages for model implementation, simulators, parameter estimation algorithms, and optimization solvers. This includes Python packages for scripting and data manipulation as well as other more comprehensive software packages for specific purposes. In particular, modeling and optimization for physical systems currently rely on the Modelica language specification.Starting Price: Free -
47
Fido
Fido
Fido is a light-weight, open-source, and highly modular C++ machine learning library. The library is targeted towards embedded electronics and robotics. Fido includes implementations of trainable neural networks, reinforcement learning methods, genetic algorithms, and a full-fledged robotic simulator. Fido also comes packaged with a human-trainable robot control system as described in Truell and Gruenstein. While the simulator is not in the most recent release, it can be found for experimentation on the simulator branch. -
48
Key Ward
Key Ward
Extract, transform, manage, & process CAD, FE, CFD, and test data effortlessly. Create automatic data pipelines for machine learning, ROM, & 3D deep learning. Removing data science barriers without coding. Key Ward's platform is the first end-to-end engineering no-code solution that redefines how engineers interact with their data, experimental & CAx. Through leveraging engineering data intelligence, our software enables engineers to easily handle their multi-source data, extract direct value with our built-in advanced analytics tools, and custom-build their machine and deep learning models, all under one platform, all with a few clicks. Automatically centralize, update, extract, sort, clean, and prepare your multi-source data for analysis, machine learning, and/or deep learning. Use our advanced analytics tools on your experimental & simulation data to correlate, find dependencies, and identify patterns.Starting Price: €9,000 per year -
49
SwiftComp
AnalySwift
SwiftComp is a revolutionary multiscale, multiphysics composite simulation code that quickly and easily delivers the accuracy of 3D FEA at the efficiency of simple engineering models. In doing so, SwiftComp reduces barriers for engineers by enabling them to model composites as easily as metals using conventional structural elements in their FEA codes (without losing accuracy while capturing all the microstructural details). SwiftComp provides unified modeling for 1D (beams), 2D (plates/shells), or 3D structures, calculating all the effective properties. Use SwiftComp either independently for virtual testing of composites or as a plug-in to power your conventional structural tools with high-fidelity composites modeling. SwiftComp can compute the best structural model for use in macroscopic structural analysis, as well as perform dehomogenization to compute the pointwise stresses in the microstructure. SwiftComp directly interfaces with ABAQUS, ANSYS. -
50
MyDataModels TADA
MyDataModels
Deploy best-in-class predictive analytics models TADA by MyDataModels helps professionals use their Small Data to enhance their business with a light, easy-to-set-up tool. TADA provides a predictive modeling solution leading to fast and usable results. Shift from days to a few hours into building ad hoc effective models with our 40% reduced time automated data preparation. Get outcomes from your data without programming or machine learning skills. Optimize your time with explainable and understandable models made of easy-to-read formulas. Turn your data into insights in a snap on any platform and create effective automated models. TADA removes the complexity of building predictive models by automating the generative machine learning process – data in, model out. Build and run machine learning models on any devices and platforms through our powerful web-based pre-processing features.Starting Price: $5347.46 per year
