RECURSION PHARMACEUTICALS, INC.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a contrastive molecular-phenomic embedding model that learns joint latent space embeddings between molecular structures and phenomic images to generate molecular-phenomic embeddings that represent molecular impacts on cellular functions. Indeed, the disclosed systems can utilize phenomic image embeddings generated from a pretrained phenomic image encoder model and corresponding molecular structural embeddings with a contrastive molecular-phenomic embedding model to learn a joint latent space between molecular structures and phenomic images (with an inter-sample similarity aware loss (S2L), explicit and implicit concentration encoding, and under sampling of inactive molecular data). In addition, the disclosed systems can utilize molecular structures and/or phenomic images with the contrastive molecular-phenomic embedding model to generate molecular-phenomic embeddings that enable a variety of molecular inferences (e.g., similar molecule determinations, similar phenomic image determinations, phenotypic impact determinations from particular molecules, and/or molecular activity classifications).

An aromatic heterocyclic compound, a pharmaceutical composition and use thereof. Specifically disclosed are a compound as shown in formula I, a stereoisomer thereof, a diastereomer thereof, or a pharmaceutically acceptable salt of any one of the foregoing, or a crystal form or solvate of any one of the foregoing. The aromatic heterocyclic compound has a novel structure, good CDK7 inhibitory activity, and good selectivity.

Disclosed are compounds, compositions and methods for treating disease, syndromes, conditions and disorders that are affected by the modulation of MALT-1. Such compounds are represented by Formula (I) and Formula (II), wherein the variables are defined herein.

The present disclosure describes compounds of Formula (I) and Formula (II) and stereoisomers, tautomers, and pharmaceutically acceptable salts or solvates thereof, wherein all of the variables are as defined herein. This disclosure also relates to pharmaceutical compositions comprising these compounds and methods of treating a disease, disorder, or condition by using the compounds and pharmaceutical compositions.

In various embodiments, a molecule exploration application determines one or more potential drug candidates during a drug discovery process. The molecule exploration application generates derived molecule specifications based on a query molecule specification and edit heuristics. Subsequently, the molecule exploration application performs, via a mapping algorithm, one or more mapping operations on the derived molecule specifications to generate mapped molecule specifications. The molecule exploration application then performs one or more search operations on a mapped catalog of molecules based on the mapped molecule specifications to determine the one or more potential drug candidates. Advantageously, the molecule exploration application can be used to efficiently determine additional drug development candidates during a drug discovery process.

A method for computational drug design includes defining a population of a plurality of compounds. Each compound includes one or more molecular properties. The method includes defining a training set of compounds from the population for which one or more biological properties are known. The method includes selecting, from the population, a subset of one or more compounds that are not in the training set. The method includes determining a subset score of the selected subset based on molecular properties of the one or more compounds in the selected subset, and evaluating the selected subset based on the determined subset score. The subset score is determined based on a frequency of the molecular properties in the population and on a frequency of the molecular properties in a sampled set comprising the training set and the selected subset.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that identifies outlier gene-compound relationships by leveraging a trained machine learning classification model and a compound-perturbation anomaly detection model. Indeed, in one or more implementations, the disclosed systems generate a plurality of compound-perturbation interaction predictions by using a machine learning classification model trained using a plurality of compound-perturbation features. For instance, the disclosed systems select a set of target features from the plurality of compound-perturbation features based on contribution values of the compound-perturbation features in generating the compound-perturbation interaction predictions. In some instances, the disclosed systems train a compound-perturbation anomaly detection model to identify outlier compound-perturbation relationships from the set of target features.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing generative thermodynamics neural networks to utilize an energy-to-base distribution transformation process to determine a binding conformation for a query compound and a target protein. For example, the disclosed systems can sample a conformation of the query compound from a known distribution and utilize the base-to-energy distribution transformation process to map the compound from the known distribution to a binding conformation. Moreover, the disclosed systems can determine an energy value associated with the binding conformation. In some bases, the disclosed systems can utilize an energy-to-base distribution transformation process to determine a binding metric for the binding conformation.

Disclosed are novel compounds according to formula (1), their pharmaceutically acceptable salts, and pharmaceutical compositions thereof. Also disclosed are methods of using such compounds and compositions to treat various diseases including cancer and proliferative diseases and disorders.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilizing compound-protein machine learning representations to generate target results. For example, the disclosed systems can utilize a compound-protein interaction machine learning model to generate a compound-protein machine learning representation for compound protein pairs. The disclosed systems can utilize the compound-protein machine learning representation to train and utilize other target machine learning models in generating predicted bioactivity results. For example, the disclosed systems train a target machine learning model from compound-protein machine learning representations to generate ADMET predictions and/or biological perturbation program predictions. Furthermore, the disclosed systems can utilize one or more explainability models in conjunction with target machine learning models trained based on compound-protein machine learning representations to identify proteins that contribute to predicted bioactivity results.