Best Drug Discovery Software in China - Page 2

Bringing a new drug into the market, from the first step to the final market introduction, is a time-consuming, highly regulated, and expensive process, which can take a decade or more. Final success crucially depends on the early availability of accurate analytical results, fast enough for taking the right decisions at the beginning of the development and minimizing late attrition rates. Today’s drug development is mainly based on a rational approach where typically establishing the biological target to focus on is the first key step. This target identification requires a deep understanding of the candidates´ properties to identify the most promising ones as quickly and reliable as possible. Once a biological target has been established, finding the most promising lead molecules is often seen as the next challenge. Typically, lead discovery is the identification of potential drug candidates – either small organic molecules or biologic assemblies with therapeutic potential.

Chemical Computing Group (CCG) has a strong reputation for collaborative scientific support. With offices in North America, Europe and Asia, our team of PhD-level scientists works closely with our clients, providing support, hands-on training and scientific advice on a wide range of projects. CCG continuously develops new technologies with its team of mathematicians, scientists and software engineers and through scientific collaborations with customers.

With all the tools you need in a single, interoperable platform, effortlessly share pre-clinical and clinical knowledge across your organization through secure and consistent workflows using Phoenix-based tools and 3rd-party applications. Phoenix WinNonlin is the first choice for non-compartmental analysis (NCA), toxicokinetic modeling, and pharmacokinetic and pharmacodynamic (PK/PD) modeling by over 6,000 researchers at biopharmaceutical companies, academic institutions, and 11 global regulatory agencies, including the US FDA, EMA, PMDA and more. The Phoenix Platform also features population PK/PD (popPK) modeling with Phoenix NLME and Level A correlation via the Phoenix IVIVC Toolkit, Validation Suites provide fast and easy software validation in under 30 minutes.

Chemia is a Browser-based & cloud-ready ELN platform. Chemia is conceived, designed and architected by scientists for scientists. A platform to drive, assign, manage and monitor all R&D activities and record capture from one dashboard. It enables you to automate your R&D set-up and make it a truly paperless operation. It saves time(approx. an hour saved per scientist) with Cross-functional collaboration; makes you audit-ready and manages data effectively. Quick retrieval, search, comparative study, and reconfigurability enable faster and appropriate decisions. An inventory management system that organizes, maintains and schedules relevant information for chemicals and equipment used inside the laboratory. A system that provides usage logs of equipment, maintenance logs and calibration logs, for effective management of labs and efficient working. A system that provides the protocol and adherence to it for compliance.

Genedata Imagence® lets you train a deep neural network to classify cellular phenotypes in HCS images for unbiased, high-quality results. It automates your analysis to put the power of deep learning algorithms in the hands of assay biologists. Genedata Imagence empowers biologists to directly and immediately analyze HCS imaging data using sophisticated deep learning techniques without any specialized algorithmic expertise. Don’t shroud your analysis under abstract lines of code. The Genedata Imagence intuitive interface allows you to easily QC and explore data every step of the way.

Genedata Biologics® streamlines discovery of biotherapeutics including bispecifics, ADCs, TCRs, CAR-Ts, and AAVs. The most widely adopted platform across the industry, it integrates all discovery workflows so you can focus on true innovation. Accelerate research with a first-in-class platform uniquely designed from the start to digitalize biotherapeutic discovery. The platform facilitates complex R&D processes by designing, tracking, testing, and assessing novel biotherapeutics drugs. It works with any format, from antibodies, bi- or multi-specifics, ADCs, novel scaffolds, and therapeutic proteins, to engineered therapeutic cell lines such as TCRs and CAR-T cells. Acting as a central end-to-end data backbone, Genedata Biologics integrates all R&D processes, from library design and immunizations, selections and panning, molecular biology, screening, protein engineering, expression, purification, and protein analytics, to candidate developability and manufacturability assessments.

When operating LC-MS/MS for research or routine workflows, you expect to achieve fast, accurate, and conclusive results. The SCIEX software suite helps you get the most out of your high-performance LC-MS/MS system. It includes specific workflow and application modules to supplement your operating system. As a result, your mass spectrometer runs with the ideal software combination, tailored to your needs. These are the core engines of SCIEX nominal mass and accurate mass LC-MS/MS systems. They are for rapid and reliable data acquisition, processing, and reporting, all with compliance readiness. Combine high performance and simplicity with add-on modules for optimized quantitative and qualitative workflows. Translate your data into conclusive results even faster with application-specific software modules.

Site-Identification by Ligand Competitive Saturation (SILCS) generates 3D maps (FragMaps) of interaction patterns for chemical functional groups with your target molecule. Site-Identification by Ligand Competitive Saturation (SILCS) generates 3D maps (FragMaps) of interaction patterns for chemical functional groups with your target molecule. SILCS reveals intricacies of dynamics and provides tools to optimize ligand scaffolds using qualitative and quantitative binding pockets insights allowing more rapid and effective drug design. SILCS uses multiple small molecule probes with various functional groups, explicit solvent modeling, and target molecule flexibility to perform protein target mapping. Visualize favorable interactions with the target macromolecule. Gain insights to design better ligands with optimally placed functional groups.

Our reputation as thought leaders in the areas of ADMET property prediction, physiologically-based pharmacokinetics (PBPK) modeling, pharmacometrics, and quantitative systems pharmacology/toxicology is earned through the success our clients have found through their relationship with us. We have the talent and 20+ years of experience to translate science into user-friendly software and provide expert consulting supporting drug discovery, clinical development research, and regulatory submissions.

AutoDock is a suite of automated docking tools. It is designed to predict how small molecules, such as substrates or drug candidates, bind to a receptor of known 3D structure. Over the years, it has been modified and improved to add new functionalities, and multiple engines have been developed. Current distributions of AutoDock consist of two generations of software: AutoDock 4 and AutoDock Vina. More recently, we developed AutoDock-GPU, an accelerated version of AutoDock4 that is hundreds of times faster than the original single-CPU docking code. AutoDock 4 actually consists of two main programs: autodock performs the docking of the ligand to a set of grids describing the target protein; autogrid pre-calculates these grids. In addition to using them for docking, the atomic affinity grids can be visualized. This can help, for example, to guide organic synthetic chemists design better binders.

Unlock hidden insights in data using the Cortellis™ suite of life science intelligence solutions – so you can make better informed decisions along the entire R&D lifecycle. We’ve removed the hard work of finding, integrating, and analyzing data so you can focus on the critical decisions needed to get your products to market faster. Applying a unique depth, breadth and quality of data that is enriched with deep domain knowledge, industry understanding, and therapeutic expertise, Cortellis unlocks hidden insights to drive data-driven decisions that accelerate innovation. Get precise, actionable answers to your specific questions across the R&D lifecycle with the broadest and deepest sources of intelligence. Accelerate innovation with Cortellis as an indispensable part of your daily workflow.

metaphactory transforms your data into consumable, contextual & actionable knowledge and drives continuous decision intelligence. Out-of-the-box, intuitive interfaces for searching, browsing & exploring your Knowledge Graph. Low-code approach to building custom interfaces that enable business-user interaction with the Knowledge Graph. Start small, iterate often & add new use cases, new data and new users on the fly. Agile knowledge management & low-code platform for building applications.

Pharma companies need comprehensive genomic information to drive successful precision medicine programs, but decisions are often made using only a fraction of the data available, about 10%. Genomenon delivers 100% of the data. An efficient and cost-effective natural history research solution for pharma, ProdigyTM Patient Landscapes support the development of rare disease therapies by enhancing insights contained in retrospective and prospective health data. Using a powerful AI-driven approach, Genomenon delivers a comprehensive and expert assessment of every patient in the published medical literature, in a fraction of the time. Don’t miss anything, get insight into every genomic biomarker published in the medical literature. Every scientific assertion is supported by empirical evidence from the medical literature. Identify all genetic drivers and pinpoint which variants are known to be pathogenic according to ACMG clinical standards.

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.

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.

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.

AIDDISON™ drug discovery software combines the power of artificial intelligence (AI), machine learning (ML), and 3D computer-aided drug design (CADD) methods to act as a valuable toolkit for medicinal chemistry needs. As a unified platform for efficient and effective ligand-based and structure-based drug design, it integrates all the facets for virtual screening and supports methods for in-silico lead discovery and lead optimization.

Makya is the first user-friendly SaaS platform for AI-driven de novo drug design focused on Multi-Parametric Optimization (MPO). It enables the design of novel and easy-to-make compounds in line with a multi-objective blueprint with unprecedented speed, performance, and diversity. Makya offers multiple generative algorithms covering different use cases from hit discovery to lead optimization: fine-tuning generator to find optimal solutions within your chemical space in line with your project blueprint; novelty generator to find new ideas with high novelty for re-scaffolding/hit discovery; forward generator to design a focused library of compounds easily accessible from commercial starting materials. The new Makya 3D module enhances the user experience and scientific utility of Makya. With an extensive set of 3D modeling features in both ligand-based and structure-based pipelines, with Makya 3D you can now calculate 3D scores and use these to guide generations natively in Makya.

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.

Proven AI-powered drug discovery. Cerella creates new value from your drug discovery data, revealing hidden insights into the best compounds and most valuable experiments for your project. It makes confident predictions, accurately filling in missing values, especially for expensive downstream experiments that can’t be predicted by other methods. This enables you to do more, even with sparse, limited data sets.

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.

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.

Automated surveillance of any data source, fully integrated with a GVP IX compliant signal management platform. GVP-IX compliant signal management platform integrated within Evidex and ready to use off-the-shelf. Modernize and audit-proof your management processes without having to move back and forth between platforms and services. Unlock the value of your safety data. When you automate signal detection and management, you can focus not just on regulatory requirements, but on driving value for your organization. Identify safety signals from traditional sources like ICSR databases, FDA Adverse Event Reporting System (FAERS), VigiBase and clinical trial data. Include new data sources such as claims, EHR, and other unstructured data. Bring these pools of information together seamlessly to enhance signaling algorithms, make validations and assessment more efficient, and provide faster answers to drug safety questions.

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.

The software that will help you improve the safety, traceability, quality, agility and integration of your Pharmacotechnics and Medicines Manufacturing Unit. No two pharmacy services are the same, which is why we have divided PharmaSuite into functional modules that will help you manage the different units in the most efficient way. Management of the Pharmacotechnics and Drug Preparation unit (parenteral mixtures). Management of parenteral nutrition.

The first pillar of our immunotherapy is our understanding of antigens and neoantigens, and specifically which ones will be transcribed, translated, processed and presented on a cell surface by Human leukocyte antigen (HLA) molecules; and therefore will be visible to T cells. We accomplish this through the use of Gritstone EDGETM, our proprietary machine learning-based platform. Developing cancer immunotherapies that include tumor-specific neoantigens presents a challenge due to their nature – tumors typically have hundreds of mutations, but only a small percentage of those mutations result in true tumor-specific neoantigens that are. To address this challenge, we trained EDGE’s novel integrated neural network model architecture with millions of data points from hundreds of tumor and normal tissue samples from patients of various ancestries.

Rare diseases are often not well studied and there is a limited understanding of many of the aspects necessary to support a drug discovery program. Our AI platform, Healnet, overcomes these challenges by analyzing millions of drug and disease data points to find novel connections that could be turned into new treatment opportunities. By applying frontier technologies across the discovery and development pipeline, we can run multiple stages in parallel and at scale. One disease, one target, one drug: it's an overly simple model, yet it's the one used by nearly all pharmaceutical companies. The next generation of drug discovery is AI-powered, parallel and hypothesis-free. Bringing together the key three drug discovery paradigms.

Kanteron Platform ingested medical images, digital pathology slides, genomics sequences, and patient data from modalities, scanners, sequencers and databases, and provided a complete data toolkit to every team in hospital networks. Pharmacogenomics for adverse medication event prevention, and Precision Medicine application at the point of care: Incorporates sources of drug-gene interaction data that were previously only available in in accessible formats (e.g. tables in a PDF document), implementing the major Pharmacogenomic databases (like PharmGKB, CGI, DGIdb, OpenTargets...) Allows the user to refine their query to certain gene families, types of interactions, classes of drugs, etc. Flexible AI means you can choose the data set that best fits your use case, and apply it to your relevant medical images.

Software to Streamline High Throughput Experiments from Design to Decide.

Predict genotoxic & carcinogenic endpoints of impurities and degradants to meet ICH M7 guidelines. Impurity Profiling Suite can be used as part of your ICH M7 workflow—to help prepare regulatory submissions and remain compliant.