Abstract: A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

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 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 patient health data prediction and analysis which utilizes an automated text mining tool to automatically format ingested electronic health record data to be added to a knowledge graph, which enriches the edges between nodes of the knowledge graph with fully interactive edge data, which can extract a subgraph of interest from the knowledge graph, and which analyzes the subgraph of interest to generate a set of variables that define the subgraph of interest. The system utilizes a knowledge graph and data analysis engine capabilities of the data platform to extract deeper insights based upon the enriched edge data.

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.

Abstract: A system and method for an automated pharmaceutical research utilizing contextual workspaces comprising a workspace drive engine, a data analysis engine, one or more machine and deep learning modules, a knowledge graph, and a workspace interface, which can create a virtual research workspace where data files containing biochemical data related to current research can be uploaded, which automatically processes and analyzes the uploaded data file to autonomously extract a plurality of information related to the uploaded data file, which performs various similarity searches on the uploaded data, and which formats and displays all the extracted information in the workspace, such that the workspace may provide a deeper contextualized view of the uploaded biochemical data.

Abstract: A system and method that produces an accurate probability distribution representative of a target molecule that may be used in pharmacokinetics and analogous applications. A generator is seeded from a variational autoencoder during training and is then used after training in series with a second variational autoencoder to produce the probability distributions from molecular tensors.

Abstract: A system and method that predicts whether a given protein-ligand pair is active or inactive, the ground-truth protein-ligand complex crystalline-structure similarity, and an associated bioactivity value. The system and method further produce 3-D visualizations of previously unknown protein-ligand pairs that show directly the importance assigned to protein-ligand interactions, the positive/negative-ness of the saliencies, and magnitude. Furthermore, the system and method make enhancements in the art by accurately predicting protein-ligand pair bioactivity from decoupled models, removing the need for docking simulations, as well as restricting attention of the machine learning between protein and ligand atoms only.

Abstract: A system and method for biomarker-outcome prediction and medical literature exploration which utilizes a data platform to analyze, optimize, and explore the knowledge contained in or derived from clinical trials. The system utilizes a knowledge graph and data analysis engine capabilities of the data platform. The knowledge graph may be used to link biomarkers with molecules, proteins, and genetic data to provide insight into the relationship between biomarkers, outcomes, and adverse events. The system uses natural language processing techniques on a large corpus of medical literature to perform advanced text mining to identify biomarkers associated with adverse events and to curate a comprehensive profile of biomarker-outcome associations. These associations may then be ranked to identify the most-common biomarker-outcome association pairs.

Abstract: A system and method that predicts whether a given protein-ligand pair is active or inactive and outputs a pose score classifying the propriety of the pose. A 3D bioactivity platform comprising a 3D bioactivity module and data platform scrapes empirical lab-based data that a docking simulator uses to generate a dataset from which a 3D-CNN model is trained. The model then may receive new protein-ligand pairs and determine a classification for the bioactivity and pose propriety of that protein-ligand pair. Furthermore, gradients relating to the binding affinity in the 3D model of the molecule may be used to generate profiles from which new protein targets may be determined.

Abstract: A system and method for computationally tractable prediction of protein-ligand interactions and their bioactivity. According to an embodiment, the system and method comprise two machine learning processing streams and concatenating their outputs. One of the machine learning streams is trained using information about ligands and their bioactivity interactions with proteins. The other machine learning stream is trained using information about proteins and their bioactivity interactions with ligands. After the machine learning algorithms for each stream have been trained, they can be used to predict the bioactivity of a given protein-ligand pair by inputting a specified ligand into the ligand processing stream and a specified protein into the protein processing stream. The machine learning algorithms of each stream predict possible protein-ligand bioactivity interactions based on the training data.

Abstract: A system and method for an automated pharmaceutical research data platform comprising a data curation platform which searches for and ingests a plurality of unstructured, heterogenous medical data sources, extracts relevant information from the ingested data sources, and creates a massive, custom-built and intricately related knowledge graph using the extracted data, and a data analysis engine which receives data queries from a user interface, conducts analyses in response to queries, and returns results based on the analyses. The system hosts a suite of modules and tools, integrated with the custom knowledge graph and accessible via the user interface, which may provide a plurality of functions such as statistical and graphical analysis, similarity based searching, and edge prediction among others.