4D- quantitative structure–activity relationship modeling: making a comeback

ABSTRACT

Introduction: Predictive Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential methodology for rapidly assessing various properties of chemicals. The vast majority of these QSAR models utilize numerical descriptors derived from the two- and/or three-dimensional structures of molecules. However, the conformation-dependent characteristics of flexible molecules and their dynamic interactions with biological target(s) is/are not encoded by these descriptors, leading to limited prediction performances and reduced interpretability. 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. That is why conformation-dependent 4D-QSAR modeling methods were developed two decades ago. However, these methods have always suffered from the associated computational cost. Recently, 4D-QSAR has been experiencing a significant come-back due to rapid advances in GPU-accelerated molecular dynamic simulations and modern machine learning techniques.

Areas covered: Herein, the authors briefly review the literature regarding 4D-QSAR modeling and describe its modern workflow called MD-QSAR. Challenges and current limitations are also highlighted.

Expert opinion: The development of hyper-predictive MD-QSAR models could represent a disruptive technology for analyzing, understanding, and optimizing dynamic protein-ligand interactions with countless applications for drug discovery and chemical toxicity assessment. Therefore, there has never been a better time and relevance for molecular modeling teams to engage in hyper-predictive MD-QSAR modeling.

KEYWORDS:

• 4D descriptors
• cheminformatics
• QSAR
• molecular dynamics

Article highlights

• 4D-QSAR has been a promising molecular modeling since its initial development by Hopfinger et al. in the 1990s

• 4D-QSAR fully takes into account ligands’ conformational flexibility in order to build predictive models for assessing their biological activities

• The rising power of GPU computing enables extremely fast parallel computation (especially for molecular dynamics simulations and deep learning) fueling the renaissance of the 4D-QSAR technology for bigger and more complex sets of chemicals

• The most recent 4D- and MD-QSAR modeling studies illustrate the higher reliability and interpretability of those models to compute the binding affinity or inhibition potency of small molecule ligands

• Complementary to 2D/3D QSAR models and 3D docking, next-generation MD-QSAR models are poised to play an essential role for AI-driven molecular design

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer Disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

D Fourches is supported and funded by the NC State Chancellor’s Faculty Excellence Program. J Ash is supported by a National Institute of Health bioinformatics training grant [T32ES007329], the Triangle Center of Evolutionary Medicine, and the SAS Institute.