Artificial Intelligence in Drug Development

ROCA, CARLOS; SEBASTIÁN-PÉREZ, VÍCTOR; MARTINEZ, MARÍA JIMENA; CRAVERO, FIORELLA; DIAZ, MÓNICA F.; PAEZ, JUAN ANTONIO; PONZONI, IGNACIO; CAMPILLO, NURIA E.

Bilbao

Encuentro; X Spanish Drug Discovery Network Meeting; 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. 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 QSPR models for the prediction of the pharmacokinetic and toxicological (ADMET) properties plays also a key role in lead optimization. We would like to present the employment of software tools based on artificial intelligence in the different key steps ofn the drug development process.