Abstract: A system and method for technology stack discovery by performing active inspection of a cloud computing environment utilizing disk cloning is described. The method includes: generating an inspectable disk based on an original disk of a reachable resource, wherein the reachable resource is a cloud object deployed in the cloud computing environment, and accessible from a network which is external to the cloud computing environment; detecting a cybersecurity object on the inspectable disk, the cybersecurity object indicating a cybersecurity issue; selecting a network path including a network protocol to access the reachable resource; and actively inspecting the network path to detect the cybersecurity issue.

Abstract: A system and method for agentless detection of sensitive data in a cloud computing environment is disclosed. The method includes: generating an inspectable disk from a clone of an original disk in a cloud computing environment; inspecting the inspectable disk for a cybersecurity object, the cybersecurity object indicating a sensitive data, the disk deployed in a cloud computing environment; extracting a data schema from the cybersecurity object, in response to detecting the cybersecurity object on the disk; generating a classification of the data schema; detecting in the disk a plurality of data files, each data file including the classified data schema; determining that the data schema corresponds to sensitive data based on the generated classification; generating in a security database: a representation of the data schema, and a representation of each data file; and rendering a visual representation of the cloud computing environment including a representation of the data schema.

Abstract: A system and method for agentless detection of sensitive data in a cloud computing environment includes generating a snapshot from a managed database service, the snapshot including a plurality of data files stored in a bucket on a cloud computing environment; detecting a data object in the plurality of data files, the data object including a data schema and a content; classifying the first data object based on the content, wherein the content is classified as sensitive data or non-sensitive data; and generating a node on a security graph stored in a graph database to represent the first data object and the classification thereof, wherein the security graph further includes a representation of the cloud computing environment.

Abstract: A system and method for agentless detection of sensitive data in a cloud computing environment. The method includes detecting a first data object including a data schema and a content in a cloud computing environment; detecting a second data object, having the data schema of the first data object; generating in a security graph: a first data object node representing the first data object, a second data object node representing the second data object, and a data schema node representing the data schema; storing a classification based on the content in the security graph, wherein the content is classified as sensitive data or non-sensitive data; and rendering an output based on the classification and the data schema node, in lieu of the first data object node and the second data object node, in response to receiving a query to detect a node representing a data object classified as sensitive data.

Abstract: A system and method for agentless detection of sensitive data in a cloud computing environment includes generating a snapshot from a managed database service, the snapshot including a plurality of data files stored in a bucket on a cloud computing environment; deploying a virtual instance based on the snapshot to generate a database, the database including a database management system (DBMS); querying the DBMS to fetch data from the database; classifying the fetched data, wherein the fetched data is classified as sensitive data or non-sensitive data; and generating a node on a security graph stored in a graph database to represent the fetched data and the classification thereof, wherein the security graph includes a representation of the cloud computing environment.

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 pharmacophore-conditioned generation of molecules. The system and method modifies a conditional variational autoencoder (CVAE) such that the latent space in generation of a molecule is not conditioned on the pharmacophore space of the molecule. This allows for generation of pharmacophore descriptors independently from the conditional on which CVAE has been trained, removing a substantial impediment to the use of CVAEs for exploration of pharmacophore descriptors of a molecule.

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 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 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 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 comprising a transmoler that identifies common substructures of a given 3D conformer and predicts its structural information. First, based on contrastive learning, substructure embeddings are learned in an unsupervised manner. Secondly, a novel oriented 3D object regressor predicts the dimensions and directions of each substructure in a conformer as well as its fingerprint embedding which are used to create differentiable junction tree molecular graphs. Lastly, using the junction tree graphs, molecular representations such as DeepSMILES are generated which represent new and novel molecules. The system may also generate conformers directly from a pocket. A pocket may be input to the model and the model learns to generate structures which can fit that pocket by conditioning the generative system. Furthermore, structure-based contrastive embeddings generated for transmoler can be recycled in structure-based generative modelling.

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.