Computational Methods for Anticancer Drug Discovery; The MCT4 Paradigm.

Abstract

Modern anticancer research has employed advanced computational techniques and artificial intelligence methods for drug discovery and development, along with the massive amount of generated clinical and in silico data over the last decades. Diverse computational techniques and state-of-the-art algorithms are being developed to enhance traditional Rational Drug Design pipelines and achieve cost-efficient and successful anticancer candidates to promote human health. Towards this direction, we have developed a pharmacophore- based drug design approach against MCT4, a member of the monocarboxylate transporter family (MCT), which is the main carrier of lactate across the membrane and highly involved in cancer cell metabolism. Specifically, MCT4 is a promising target for therapeutic strategies as it overexpresses in glycolytic tumors, and its inhibition has shown promising anticancer effects. Due to the lack of experimentally determined structure, we have elucidated the key features of the protein through an in silico drug design strategy, including for molecular modelling, molecular dynamics, and pharmacophore elucidation, towards the identification of specific inhibitors as a novel anti-cancer strategy.