Machine Learning approaches to improve prediction of target-drug interactions

BALATTI, GALO E.; BARLETTA, PATRICIO G.; PEREZ, ANDRES, D.; GIUDICESSI, SILVANA L.; MARIA C MARTINEZ CERON

Design of New Drugs Using Machine Learning

Scrivener Publishing, Wiley

Lugar: Beverly, MA; Año: 2022; p. 1 - 77

In the initial steps of early drug discovery, the traditional techniques like docking, QSAR or Molecular Dynamics have been used for decades identifying targets, ranking molecule candidates and optimizing the lead compounds chemically to decrease toxicity and improve drug ADME (absorption, distribution, metabolism, and excretion) properties. Nowadays, computational tools are increasingly used not only in the drug discovery process, but also in drug development. Information technologies like Artificial Intelligence (AI) and Machine Learning (ML) participate in practically every step in the pharma value chain, improving and accelerating the overall drug development and design. Besides the rise of new methodologies, the improving of relative old computational techniques like docking, QSAR or cavity search with new ML-based algorithms like Random Forest, Support Vector Machines or Neural Networks are being developing and promises to reduce times and operational costs mainly in the drug design process.In this chapter, we review some of these approaches, briefly introducing the most used ML techniques in study drug-target interactions. Also, an especial section about peptide-based drugs and its advantages over small organic molecules is discussed.