Bio-Mol:Pretraining Multimodality Bioactivity Profile for Enhancing Small Molecule Property Prediction

Abstract

Non-optimized pharmacokinetic parameters serve as the primary cause of failure in clinical trials of drugs. Therefore, the successful prediction of pharmacokinetic parameters during the pre-clinical stage is crucial for the success of drug candidates. Conventional methods primarily rely on 2D structural information, while advanced models extend the features to other structural-related information or use advanced computational models to improve prediction accuracy. However, to gain a comprehensive understanding of small molecules, integrating bioactivity profiles with chemical structural information is essential. One significant challenge in this integration is the high proportion of missing values within experimentally validated bioactivity profiles for most small molecules. To address this challenge, we introduce Bio-Mol, an artificial intelligence model designed to effectively handle this issue. Bio-Mol utilizes a pretrain and finetune strategy, enabling the incorporation of a large proportion of missing bioactivity profiles during the small molecule representation learning process. Comprehensive evaluations of Bio-Mol demonstrate a notable improvement in predicting molecule properties. The integration of missing bioactivity profiles enhances the AUROC of average 5.2% compared to the previous state-of-the-art models predictions. Furthermore, we explore the potential of Bio-Mol in predicting synergistic drug combinations, highlighting its versatility and broader applications in the field of drug discovery. The successful implementation of Bio-Mol showcases its efficacy in over-coming the challenges posed by missing bioactivity profile data. This model paves the way for optimizing small molecule pharmacokinetics prediction, providing valuable insights for drug development and discovery processes.