Abstract: A system and method for de novo drug discovery using machine learning algorithms. In a preferred embodiment, de novo drug discovery is performed via data enrichment and interpolation/perturbation of molecule models within the latent space, wherein molecules with certain characteristics can be generated and tested in relation to one or more targeted receptors. Filtering methods may be used to determine active novel molecules by filtering out non-active molecules and contain activity predictors to better navigate the molecule-receptor domain. The system may comprise neural networks trained to reconstruct known ligand-receptors pairs and from the reconstruction model interpolate and perturb the model such that novel and unique molecules are discovered. A second preferred embodiment trains a variational autoencoder coupled with a bioactivity model to predict molecules exhibiting a range of desired properties.

Abstract: A system and method for optimizing the latent space in generative machine learning models, and applications of the optimizations for use in the de novo generation of molecules for both ligand-based and pocket-based generation. The ligand-based optimizations comprise a tunable reward system based on a multi-property model and further define new measurable metrics: molecular novelty and uniqueness. The pocket-based optimizations comprise an initial multi-property optimization followed up by either a seed-based optimization or a relaxed-based optimization.

Abstract: A system and method for optimizing the latent space in generative machine learning models, and applications of the optimizations for use in the de novo generation of molecules for both ligand-based and pocket-based generation. The ligand-based optimizations comprise a tunable reward system based on a multi-property model and further define new measurable metrics: molecular novelty and uniqueness. The pocket-based optimizations comprise an initial multi-property optimization followed up by either a seed-based optimization or a relaxed-based optimization.

Abstract: A system and method for accelerating the calculations of free energy differences by automating FEP-path-decision-making and replacing the standard series of alchemical interpolations typically created by molecular dynamic (MD) simulations with voxelated interpolated states. A novel machine learning approach comprising a restricted variational autoencoder (ResVAE) is used which can reduce the computational-cost associated with interpolations by restricting the dimensions of a molecular latent space. The ResVAE generates a model based on flow-based transformations of a 3D-VAE latent point that is trained to maximize the log-likelihood of MD samples which enables the model to compute transformations more efficiently between molecules and also handle deletions of atoms more efficiently during iterative FEP calculation steps.

Abstract: A system and method for optimizing the latent space in generative machine learning models, and applications of the optimizations for use in the de novo generation of molecules for both ligand-based and pocket-based generation. The ligand-based optimizations comprise a tunable reward system based on a multi-property model and further define new measurable metrics: molecular novelty and uniqueness. The pocket-based optimizations comprise an initial multi-property optimization followed up by either a seed-based optimization or a relaxed-based optimization.

Abstract: A system and method for feedback-driven automated drug discovery which combines machine learning algorithms with automated research facilities and equipment to make the process of drug discovery more data driven and less reliant on intuitive decision-making by experts. In an embodiment, the system comprises automated research equipment configured to perform automated assays of chemical compounds, a data platform comprising drug databases and an analysis engine, a bioactivity and de novo modules operating on the data platform, and a retrosynthesis system operating on the drug discovery platform, all configured in a feedback loop that drives drug discovery by using the outcome of assays performed on the automated research equipment to feed the bioactivity module and retrosynthesis systems, which identify new molecules for testing by the automated research equipment.

Abstract: A system and method for accelerating the calculations of free energy differences by automating FEP-path-decision-making and replacing the standard series of alchemical interpolations typically created by molecular dynamic (MD) simulations with voxelated interpolated states. A novel machine learning approach comprising a restricted variational autoencoder (ResVAE) is used which can reduce the computational-cost associated with interpolations by restricting the dimensions of a molecular latent space. The ResVAE generates a model based on flow-based transformations of a 3D-VAE latent point that is trained to maximize the log-likelihood of MD samples which enables the model to compute transformations more efficiently between molecules and also handle deletions of atoms more efficiently during iterative FEP calculation steps.

Abstract: A system and method that takes in a data set comprising molecular structure data and properties of interest, e.g., ADMET, EC50, IC50, etc., and determines the substructures that cause or do not cause the property of interest. The substructures may then be used to filter out potentially harmful new proposed/generated molecules or create a new data set of known active/inactive substructures of a property of interest that may fulfill other obligations. The system comprises a substructure extraction module which further comprises a scaffold extraction module and a comparison module. A scaffold extraction module clusters, searches, and extracts substructures in question while a comparison module compares the bioactivity of each molecule with and without each substructure in question to determine the substructures effect on the property of interest.

Abstract: A system and method for de novo drug discovery using machine learning algorithms. In a preferred embodiment, de novo drug discovery is performed via data enrichment and interpolation/perturbation of molecule models within the latent space, wherein molecules with certain characteristics can be generated and tested in relation to one or more targeted receptors. Filtering methods may be used to determine active novel molecules by filtering out non-active molecules and contain activity predictors to better navigate the molecule-receptor domain. The system may comprise neural networks trained to reconstruct known ligand-receptors pairs and from the reconstruction model interpolate and perturb the model such that novel and unique molecules are discovered. A second preferred embodiment trains a variational autoencoder coupled with a bioactivity model to predict molecules exhibiting a range of desired properties.

Abstract: A system and method for automated retrosynthesis which can reliably identify valid and practical precursors and reaction pathways. The methodology involves a k-beam recursive process wherein at each stage of recursion, retrosynthesis is performed using a library of molecule disconnection rules to identify possible precursor sets, validation of the top k precursor sets is performed using a transformer-based forward reaction prediction scoring system, the best candidate of the top k precursor sets is selected, and a database is searched to determine whether the precursors are commercially available. The recursion process is repeated until a valid chain of chemical reactions is found wherein all precursors necessary to synthesize the target molecule are found to be commercially available.

Abstract: A system and method that takes in a data set comprising molecular structure data and properties of interest, e.g., ADMET, EC50, IC50, etc., and determines the substructures that cause or do not cause the property of interest. The substructures may then be used to filter out potentially harmful new proposed/generated molecules or create a new data set of known active/inactive substructures of a property of interest that may fulfill other obligations. The system comprises a substructure extraction module which further comprises a scaffold extraction module and a comparison module. A scaffold extraction module clusters, searches, and extracts substructures in question while a comparison module compares the bioactivity of each molecule with and without each substructure in question to determine the substructures effect on the property of interest.

Abstract: A system and method for automated retrosynthesis which can reliably identify valid and practical precursors and reaction pathways. The methodology involves a k-beam recursive process wherein at each stage of recursion, retrosynthesis is performed using a library of molecule disconnection rules to identify possible precursor sets, validation of the top k precursor sets is performed using a transformer-based forward reaction prediction scoring system, the best candidate of the top k precursor sets is selected, and a database is searched to determine whether the precursors are commercially available. The recursion process is repeated until a valid chain of chemical reactions is found wherein all precursors necessary to synthesize the target molecule are found to be commercially available.