Audience
Laboratories searching for a dynamic molecule design software to create end-to-end drug design workflows
About SILCS
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.
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Company Information
SilcsBio
Founded: 2012
United States
silcsbio.com/software/
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Iktos
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.
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Aurora Drug Discovery
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.
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Atomwise
We use our AI engine to transform drug discovery. Our discoveries help create better medicines faster. Our AI-enabled discovery portfolio includes wholly-owned and co-developed pipeline assets, and is backed by prominent investors. Atomwise developed a machine-learning-based discovery engine that combines the power of convolutional neural networks with massive chemical libraries to discover new small-molecule medicines. The secret to reinventing drug discovery with AI is people. We are dedicated to developing the best AI platform and using it to transform small molecule drug discovery. We have to tackle the most challenging, seemingly impossible targets and streamline the drug discovery process to give drug developers more shots on goal. Computational efficiency enables screening of trillions of compounds in silico, increasing the likelihood of success. Demonstrated exquisite model accuracy, overcoming the challenge of false positives.
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Bruker Drug Discovery
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.
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