Machine learning systems for automated pharmaceutical molecule screening and scoring

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. In some implementations, the subset may be ranked using conjoint analysis and machine learning.