Grand Challenges of Computer-Aided Drug Design: The Road Ahead

Abstract

Computer-aided drug discovery (CADD) has become an essential part of several projects in different settings and research environments. CADD has largely contributed to identifying and optimizing hit compounds leading them to advanced stages of the drug discovery pipeline or the market (PrietoMartínez et al., 2019). CADD includes several theoretical disciplines, including chemoinformatics, bioinformatics, molecular modeling, and data mining, among others (López-López et al., 2021). Artificial intelligence (AI) that has been used since the 60 s (Gasteiger, 2020) in drug discovery is regaining momentum, in particular with machine learning (ML) and deep learning (DL) (Bajorath, 2021; Bender and Cortés-Ciriano, 2021). In parallel to the continued contribution of CADD, several methodologies used in CADD have entered the hype cycle with waives of hope, inflated expectations, disappointments, and productive applications. The disillusionments are frequently driven by fashion, exacerbated misuse, and a lack of proper training to interpret the results (MedinaFranco et al., 2021). Examples are quantitative structure-activity relationship studies (QSAR). A few decades ago, there was a hype for QSAR studies; but uneducated use, bad practices, and poor reporting led to inflated expectations and disappointment (Johnson, 2008). As part of the hype, scientific journals containing the word “QSAR” in the title emerged, and years later, some journals were re-named. Molecular docking is another example of a method that is often misused, leading to false expectations and disappointments, not because the technique is not useful but because it is tried to be used for purposes that was not initially designed (e.g., correlation of docking cores with experimental binding affinities). At the time of writing this manuscript, there is a hype for AI, ML, DL; quoting Bajorath, an “AI ecstasy” (Bajorath, 2021). Despite the contributions of CADD in different stages of the drug discovery pipelines and technological advances, there are challenges that need to be addressed. Table 1 outlines the grand challenges that face drug discovery using in silicomethods and AI and are further commented on in this manuscript. The list of topics is not exhaustive; the selected challenges are based on the author’s opinion, and it is intended to be a reference for a continued update. Here, the challenges are organized into six sections. The first two are related to the chemical and biological relevant chemical spaces, respectively; that is, what spaces are being explored? Another section covers methodological challenges: how is being conducted the search for new and better drugs at the intersection of the relevant chemical and biological spaces? The next three sections present hurdles associated with communication and Human interaction in research teams, scientific dissemination, data sharing, and education, respectively. The last section contains the Conclusions.