Accelerating Covalent Drug Discovery: Recent Advances in Covalent Docking Tools

High-Throughput Technologies for Targeted Covalent Drug Discovery: Recent Advances and Perspectives.

Quantum-Driven High-Throughput Docking: Advances in Molecular Scoring and Drug Discovery.

Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.

Cysteine-targeting covalent inhibition: Recent trends and emerging strategies.

In the field of drug discovery, targeted covalent inhibitors (TCIs) have become popular due to their ability to form irreversible or reversible covalent bonds with target proteins, most frequently with cysteine. Covalent bonds offer prolonged target engagement, improved potency, and, in some cases, the ability to target proteins previously considered undruggable. TCIs can help in target validation and in obtaining information about protein function and binding sites. However, the development of TCIs is still very complex: it requires fine-tuning the warhead's reactivity to avoid off-target effects and ensure the compound's stability. This review addresses design and chemistry principles of cysteine-targeting covalent inhibitors, with a focus on the diversity of electrophilic warheads currently used or under research. Established motifs are discussed alongside newfound warheads. Recent advances in reversible covalent inhibition, metabolically labile warheads, and fragment-based electrophile screening are also addressed as strategies to improve TCIs specificity and safety. Despite the recent and substantial progress in the field, selectivity and safety remain major limitations for a broad application of TCIs. Therefore, continued innovation in warhead design is critical to develop new TCIs.

Advances in the emerging field of epi-informatics in drug discovery: roads taken and future prospects.

INTRODUCTION Epigenetic drug discovery remains a promising drug discovery strategy that has long been driven by advances in computational approaches. The subfield of epi-informatics, established more than a decade ago, continues to evolve rapidly as emerging machine learning methodologies reshape and expand its applications. AREAS COVERED The authors provide an updated overview of bioinformatics, chemoinformatics, and machine learning methodologies used to identify, design, and optimize compounds, primarily small-molecules, that modulate epigenetic processes with therapeutic potential. The discussion is based on a comprehensive literature analysis of peer-reviewed literature, encompassing 7,185 unique research articles published between 2000 up to 2025. The article also examines the epigenetic drug discovery landscape by analyzing the most extensively investigated epigenetic targets and emerging research trends. EXPERT OPINION Epi-informatics has evolved into a distinct interdisciplinary field integrating bioinformatics, chemoinformatics, and artificial intelligence to advance epigenetic drug discovery. Although rapid progress in multi-omics integration, molecular modeling, and generative artificial intelligence is accelerating the identification of drug candidates, future success will depend on high-quality, standardized data, interpretable machine learning models, and rigorous experimental validation that ensure reproducibility. Addressing these challenges will further advance epi-informatics in oncology research and an expanding range of complex diseases.

FlexAutoDock: A Flexible Platform for Automated Molecular Docking and Virtual Screening of Natural and Synthetic Compounds

FlexAutoDock is an automated cloud-based molecular docking platform that provides a unified environment for protein-ligand docking and large-scale virtual screening, providing researchers with an accessible computational resource for accelerating early-stage drug discovery.

Precision covalent chemistry: Advances in selectivity-driven covalent drug design over the past five years.

Covalent inhibitors bind tightly and persistently to protein targets via covalent links with nucleophilic amino acids, yet unintended covalent modification of irrelevant proteins creates major safety risks and restricts their clinical use. To tackle this issue, researchers have shifted from reactivity-centered design to selectivity-prioritized engineering, a core trend in this field over the last five years. This review summarizes five synergistic tactics to boost covalent inhibitor selectivity. First, strengthening noncovalent binding affinity accurately positions reactive warheads for target residues and lowers off-target interactions. Second, redesigned warheads - new electrophiles for non-cysteine sites and reversible covalent groups with adjustable binding duration - broaden druggable proteins and separate target and off-target binding via kinetic differences. Third, leveraging distinct nucleophilic microenvironments (isoform-specific amino acid variations, allosteric cavities, mutation-generated residues) enhances target-specific recognition. Fourth, structure-based tuning of warhead spatial shapes controls covalent reaction efficiency and selectivity. Fifth, prodrugs deliver active inhibitors locally at disease sites with temporal and spatial precision. Collectively, these innovations validate kinact/Ki as a unified rule balancing efficacy and selectivity. With advancing proteome profiling, computational warhead modeling and conditional electrophile chemistry, covalent inhibitors will tackle hard-to-drug targets with safety comparable to noncovalent medicines.