ARTIFICIAL INTELLIGENCE IN DRUG DEVELOPMENT

MARÍA JIMENA MARTÍNEZ; PAEZ J.A.; ROCA C.; FIORELLA CRAVERO; IGNACIO PONZONI; SEBASTIAN V.; MONICA F. DIAZ; CAMPILLO N.E.

Bilbao

Congreso; X meeting of the Spanish Drug Discovery Network; 2018

Spanish Drug Discovery Network

Artificial Intelligence (AI) has recently become an essential part of the technology industry, solving many challenging problems in computer sciences. Also, the biopharmaceutical industry is looking toward AI to speed up drug discovery, cut R&D costs, decrease failure rates in drug trials and create better medicines. In this sense, machine learning (ML) approaches have emerged as very powerful tools that can be applied in several steps of the iterative drug discovery process, such as Quantitative Structure Activity Relationship (QSAR) for the prediction of activity of large untested databases, discovery of hit compounds or synthesis prioritization for lead optimization. In order to reduce attrition rate in later stages of drug discovery and avoid compounds with undesirable properties, the development of Quantitative Structure Property Relationship (QSPR) approaches for the prediction of the pharmacokinetic and toxicological (ADMET) profile plays also a key role in lead optimization.Machine learning techniques are key tools to drug development. Thanks to this work we show that ML can be applied in several steps of the drug development process: -to develop a QSAR model of BACE1 inhibitors with a wide applicability domain.-to optimize different steps of the hit and ADME process, such as chemical synthesis and blood brain barrier or human intestinal absorption in physicochemical properties assessments.Future work applying deep learning techniques could improve the performance of AI methods in drug discovery and development.