Aim: Irreversible covalent drug inhibition is an emerging paradigm; however, critical gaps in unraveling the efficacy of molecular determinants still persist. Methodology: We compare two ERK2 inhibitors with different binding modes. A 5-7-Oxozeaenol is selective inhibitor which irreversibly binds ERK2 by the formation of covalent bond with Cys166 while 5-iodotubercidin binds noncovalently. Result & discussion: Covalent inhibition showed greater protein stability, favorable binding energetics (irreversible inhibition binding free energy [ΔGbind] = -40.4354 kcal/mol and reversible inhibition ΔGbind = -26.2515 kcal/mol); higher correlation in residual movement and multiple van der Waals interactions as evident from residue interaction analysis. Conclusion: This investigation of the different inhibition modes of ERK2 would assist toward the design of more potent and highly site-specific covalent inhibitors in cancer therapy. Graphical abstract

The serendipitous discovery of covalent inhibitors and their characteristic potency of inducing irreversible and complete inhibition in therapeutic targets have caused a paradigm shift from the use of non-covalent drugs in disease treatment. This has caused a significant evolution in the field of covalent targeting to understand their inhibitory mechanisms and facilitate the systemic design of novel covalent modifiers for ‘undruggable’ targets. Computational techniques have evolved over the years and have significantly contributed to the process of drug discovery by mirroring the pattern of biological occurrences thereby providing insights into the dynamics and conformational transitions associated with biomolecular interactions. Moreover, our previous contributions towards the systematic design of selective covalent modifiers have revealed the various setbacks associated with the use of these conventional techniques in the study of covalent systems, hence there is a need for distinct approaches. In this review, we highlight the modifications and development of computational techniques suitable for covalent systems, their lapses, shortcomings and recent advancements.

Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R² = 0.991 and Q² = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R² = 0.915 and Q² = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.

Aim: Irreversible covalent inhibition of biological targets in disease pathogenesis is an emerging field in drug design. Computational techniques have assumed a critical role in understanding covalent enzyme inhibition. However, a gap currently exists with regards to the reliability and reproducibility of currently available protocols available in literature and open scientific forums. Methodology/results: Appropriate ligand and protein target are selected, docked covalently or noncovalently using respective docking tools. Both components are subjected to premolecular dynamic preparations. This was followed by parameterization of the ligand, protein and covalent complex, respectively. The production runs were initiated and the resulting trajectories are saved and analyzed. Conclusion: This protocol is reliable and reproducible, hence would advance the development of irreversible covalent inhibitors toward disease treatment.

Introduction: Over the years, not a single HSP inhibitor has progressed into the post-market phase of drug development despite the success recorded in various pre-clinical and clinical studies. The inability of existing drugs to specifically target oncogenic HSPs has majorly accounted for these setbacks. Recent combinatorial strategies that incorporated computer-aided drug design (CADD) techniques are geared towards the development of highly specific HSP inhibitors with increased activities and minimal toxicities. Areas covered: In this review, strategic therapeutic approaches that have recently aided the development of selective HSP inhibitors were highlighted. Also, the significant contributions of CADD techniques over the years were discussed in detail. This article further describes promising computational paradigms and their applications towards the discovery of highly specific inhibitors of oncogenic HSPs. Expert opinion: The recent shift towards highly selective and specific HSP inhibition has shown great promise as evidenced by the development of paralog/isoform-selective HSP drugs. It could be further augmented with computer-aided drug design strategies, which incorporate reliable methods that would greatly enhance the design and optimization of novel inhibitors with improved activities and minimal toxicities.