Abstract
Degree of attention to protein-protein interaction (PPI) are increasing under circumstances of
the exhaustion of conventional drug targets such as enzymes, receptors and channels whose ligands
are non-protein. However, identification of potent small molecule PPI modulators are still
challenging for pharmaceutical industries because in-house chemical libraries of pharmaceutical
companies do not include strong and specific binders to PPI targets, and it is difficult to identify
active compounds by high-throughput screening (HTS). On the other hand, to improve low
productivity of drug research and developments (R&D), which is one of the most important longstanding issues for pharmaceutical industries, application of artificial intelligence (AI) to many
stages of drug R&D is being tried by large number of pharmaceutical companies, academic
institutions and biotech companies. However, a part of researchers of pharmaceutical companies
also have opinions with a skeptic tone for the contribution of AI to the improvement of low
productivity of drug R&D. Based on these situations, we review current approaches of application
of AI to drug discovery researches and introduce Interprotein’s approach to identify potent small
molecule PPI modulators with an AI-based activity prediction system.
