Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify molecules or compounds for use in pharmaceuticals. In aspects, one or more machine learning (ML) models may be trained to identify molecules based on pharmaceutical data that indicates properties of previously-identified pharmaceutical molecules, such as physiochemical structure, side effects, toxicity, solubility, and the like. The ML models may include generative models, such as generative adversarial networks or variational autoencoders. The trained ML models may be used to identify new (e.g., previously-unidentified) molecules, or the trained ML models may be provided to client devices for use in molecule identification (e.g., drug discovery).

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to screen candidate pharmaceutical molecules or compounds for pharmaceutical uses (e.g., treating diseases or conditions). The screening may be based on structural similarity between the candidate pharmaceutical molecules and various pharmaceutical targets (e.g., proteins, nucleic acids, etc.). In aspects, one or more machine learning (ML) models may be trained to assign a candidate pharmaceutical molecule to one of multiple clusters based on chemical/physical properties of the candidate pharmaceutical molecule and chemical/physical properties of pharmaceutical targets associated with the clusters. The pharmaceutical targets associated with the cluster may be scored based on comparisons between the candidate pharmaceutical molecule and the pharmaceutical target, and a subset of the pharmaceutical targets may be identified based on the scores.