Viral proteases are central targets in antiviral drug discovery and development because they play essential roles in viral replication and maturation. Although protease inhibitors have achieved major clinical success, traditional design strategies face challenges, including resistance development, poor oral exposure of early peptidomimetics, and off-target toxicity of highly reactive covalent warheads. Classical approaches, such as peptidomimetics, macrocyclization, and covalent warhead engineering, are discussed alongside contemporary strategies, including allosteric modulation and targeted protease degradation via proteolysis-targeting chimeras (PROTAC) technology. Particular emphasis is placed on how these strategies address key obstacles, such as resistance evolution, selectivity, metabolic stability, and oral bioavailability. Several quantitative case studies have also demonstrated the growing significance of computational tools in contemporary antiviral discovery. For SARS-CoV-2 main protease (Mpro), these workflows were enabled by the rapid availability of high-resolution experimental crystal structures of the target protein. The evolution of a weak fragment (Kd ≈ 1.7 mM; ΔG ≈ -3.6 kcal/mol) into a covalent inhibitor (QUB-00006-Int-07) with enzymatic inhibition (IC50 ≈ 830 nM) was successfully guided by molecular dynamics (MD) simulations and absolute binding free energy calculations. This was subsequently confirmed experimentally using NMR, ESI-MS, and FRET assays. Furthermore, out of 25 computationally prioritized candidates with Ki values less than 4 μM, 15 active Mpro inhibitors were identified using accelerated free-energy perturbation-based repurposing campaigns. Long-range allosteric pathways connecting the catalytic site to resistance-associated regions and experimentally verified allosteric pockets have also been discovered using dynamic nonequilibrium MD. Together, these integrated in silico approaches enable the early prioritization of high-affinity ligands, mechanistic understanding of resistance, and significant reduction of late-stage attrition in antiviral drug discovery. Through detailed case studies on SARS-CoV-2 main protease (Mpro), Zika virus NS2B-NS3 protease, and Dengue virus NS2B-NS3 protease, the review illustrates how medicinal chemistry principles translate molecular insights into clinically relevant antivirals. Finally, a forward-looking development roadmap is proposed that integrates potency, selectivity, pharmacokinetics, manufacturability, and resistance management toward the goal of broad-spectrum, durable, and adaptable protease-targeted therapeutics development.

Ginsenoside Rb1 as a multi-target modulator in heart failure: Mechanistic insights into extracellular remodeling and transcriptional pathways from network pharmacology, molecular dynamics, and binding free energy analyses.

Integrated virtual screening, machine learning and molecular dynamics identify novel phytochemical FabI inhibitors against MRSA.

Discovery of novel ULK1 inhibitors by virtual screening, synthesis, in vitro assay and molecular dynamics simulations.

Site-specific phosphorylation modulates p16/CDK4 binding dynamics and energetics: Insights from molecular simulations.

Dual inhibition of AChE and GSK-3β by flavonoids of Bergenia ciliata: Molecular dynamics insights into anti-Alzheimer's activity.

Design, synthesis and activity evaluation of a novel PPARα agonist based on virtual screening.

Quinolone-inspired MCM2-7 helicase inhibitors: Computational design, dynamic stability, and preclinical promise for targeted anticancer therapy.

Host-guest complexation of chlorzoxazone and its synthetic impurity 2-Amino-4-Chlorophenol by cationic water-soluble Pillar[5]arene

Computational identification of potential MMP-2 inhibitors in cancer using machine learning, molecular docking, and dynamics simulations.

LC-MS/MS analysis of polar pesticides in mango: enantiomers, docking, and retention mechanisms.

Absolute binding free energy (ΔG) calculations can rank structurally diverse compounds, which could be useful for early-stage drug discovery. Unfortunately, for flexible systems, it can be challenging to sample the receptor conformations necessary to obtain converged ΔG calculations. Here, we address this challenge by leveraging extensive molecular dynamics simulations of apo SARS-CoV-2 main protease (MPro) that were conducted on the Folding@Home distributed computing system. A Markov state model (MSM) was built to compute the equilibrium probability of each snapshot. Representative snapshots were selected from clusters defined based on occupancy fingerprints of the catalytic site. The binding potential of mean force (BPMF), the binding free energy between a ligand and rigid receptor configuration, was computed between the representative snapshots and 130 drug leads from the COVID Moonshot, an open-source drug discovery project. ΔGs were computed using an exponential average of BPMFs based on implicit ligand theory (ILT). ΔG calculations recapitulated experimental values with a Pearson R of 0.55 and a mean-adjusted root-mean-square error of 1.6 kcal/mol. Accuracy and computational costs were found to be intermediate between docking and previous free energy calculations with a fully flexible receptor. Moreover, in 88% of systems, the calculated ΔG of the native binding pose (RMSD from crystallographic <3 Å) was within 1 kT of the top-ranked pose.

Antioxidant Potential of Porphyrin Derivatives Revealed by Electrochemical, Spectroscopic, and Computational Studies.

AlphaFold-driven structure-guided identification of NS5 RdRp-targeting antiviral leads against Kyasanur Forest Disease Virus.

Apurinic/apyrimidinic endonuclease 1 (APE1) is a key enzyme in the base excision repair (BER) pathway, and its aberrant overexpression is closely associated with poor prognosis, enhanced invasiveness, and therapeutic resistance in multiple cancers, making it an attractive anticancer drug target. In this study, a systematic structure-based virtual screening workflow was established using the crystal structure of the APE1 endonuclease active pocket (PDB ID: 7TC2) to screen approximately 1.529million small molecules collected from the DrugBank, TargetMol, Specs, and ChemDiv databases. Through multi-step filtering involving drug-likeness evaluation, molecular docking, interaction fingerprint (IFP) screening, conformational strain energy assessment, and MM/GBSA binding free energy calculations, a set of candidate compounds with predicted affinity toward APE1 was identified. Representative hit compounds from different databases were further subjected to binding mode analysis and 100 ns molecular dynamics simulations. Computational analyses suggested that these candidate compounds were able to maintain hydrogen-bonding and hydrophobic interactions with key residues in the APE1 active site. Among them, DB02187 and T9286 exhibited more favorable computational performance than the reference ligand in multiple dimensions, including complex stability, per-residue energy contribution, and free energy landscape profiles. In contrast, although HIT107168463 exhibited favorable static binding energy, it showed relatively poor dynamic stability. Overall, DB02187 and T9286 displayed favorable predicted binding features and may represent candidate scaffolds for future experimental validation and lead optimization.

Relative binding free energy (RBFE) calculations are a cornerstone of rational hit-to-lead and lead optimization in modern drug discovery. However, the high computational cost and limited reliability in tackling large or complex molecular transformations often prevent their routine, high-throughput use. Here we introduce Dual-LAO, a novel, highly efficient method for calculating RBFE. Building on the Lambda-ABF-OPES framework, this method combines a dual-topology setup and suitable restraints to dramatically accelerate free energy convergence. We demonstrate that Dual-LAO, in combination with the AMOEBA polarizable force field, achieves an unprecedented acceleration factor of 15 to 30 times compared to current state-of-the-art methods on standard drug targets. Crucially, the approach maintains high accuracy and successfully tackles previously prohibitive molecular changes, including scaffold-hopping, buried water displacement, charge changes, ring-opening, and binding pose perturbations. This significant leap in efficiency allows for the widespread, routine integration of predictive molecular simulations into the rapid optimization cycles of drug discovery, enabling chemists to confidently model historically challenging systems in timescales compatible with real-world project deadlines.

Alzheimer's disease (AD) is the fourth leading cause of death among elderly people worldwide. It has a complex pathogenesis, making multitarget-directed ligands (MTDLs) a key therapeutic strategy. This study evaluated pregnane glycosides isolated from Caralluma species (Apocynaceae) as potential cholinesterase inhibitors targeting acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzymes for AD treatment. In silico molecular docking against AChE (PDB: 4EY7) and BuChE (PDB: 8CGO) identified caratuberside E and awdelioside B as top AChE binders (−11.09 and −11.49 kcal mol−1, outperforming the cocrystal inhibitor at −9.52 kcal mol−1). For BuChE, caratuberside G and penicilloside C showed superior scores (−10.94 and −11.55 kcal mol−1vs. −8.89 kcal mol−1 for the cocrystal). These results were validated by 200 ns molecular dynamics simulations (stable RMSD values) and MM-GBSA binding free-energy calculations, confirming strong interactions and favourable energetics. In vitro assays (using donepezil as reference) demonstrated potent inhibition: caratuberside E was most active against AChE (IC50 = 0.69 ± 0.07 µM), followed by awdelioside B (IC50 = 18.99 ± 0.06 µM); caratuberside G (IC50 = 1.59 ± 0.16 µM) and penicilloside C (IC50 = 12.38 ± 0.51 µM) excelled against BuChE. Collectively, these pregnane glycosides from Caralluma show promise as selective cholinesterase inhibitors and potential MTDLs for AD therapy.

Docking-based virtual screening (VS) is essential for hit finding in the initial stage of drug or probe discovery. However, it remains prone to high false-positive rates, often resulting in unsuccessful screening campaigns. MD-based alchemical free-energy methods offer a promising solution to improve VS hit rates but are highly resource-intensive. Real-world and benchmark studies incorporating alchemical absolute binding free energy (ABFE) calculations could help optimize their use in VS pipelines. Here, we present a large-scale benchmark to evaluate the comparative value of ABFE calculations in VS workflows. Two data sets were used: a curated set of 632 ligand-protein complexes from the PDBbind database to assess ABFE quantitative accuracy and a set of 315 binders and decoys from the Database of Useful Decoys (DUD-E) to evaluate predictive power in a VS context. Alongside alchemical ABFE, we benchmarked computationally affordable end-state physics-based methods and five machine-learning (ML) models. The study ranked BFE predictors consistently with their computational cost, with alchemical ABFE performing well across both benchmarks. End-state methods scored well in recognizing actives from decoys in the DUD-E data set but showed little correlation with experimental values in PDBbind. Most ML models performed well on PDBbind, likely due to training overlap, but failed on DUD-E, except for GNINA and Boltz-2, which demonstrated a degree of generalization comparable to end-state physics-based methods. Overall, a staged approach involving Boltz-2 as a primary filter followed by alchemical ABFE is likely to robustly and cost-efficiently enrich docking-based VS hit lists with true actives.

Molecular basis of BACE1 modulation revealed by machine learning, molecular simulations, and experimental validation.

Phosphorylation of Thr221 (T221) in salt-inducible kinase 3 (SIK3) is a key determinant of its catalytic activity, with broad implications ranging from sleep homeostasis to tumorigenesis. Despite its physiological significance, however, the underlying molecular mechanism by which this phosphorylation event regulates enzymatic activity remains poorly understood. Here, we combine all-atom molecular dynamics (MD) simulations, quantum mechanics/molecular mechanics (QM/MM)-based steered molecular dynamics (SMD) simulations, molecular mechanics/generalized Born surface area (MM/GBSA) binding free-energy calculations, protein contact network (PCN) analysis, and principal component analysis (PCA) to systematically elucidate the allosteric effects of T221 phosphorylation. We show that a highly occupied pT221-Arg112 salt bridge stabilizes the αC-helix in its "in" conformation and strengthens the conserved Glu113(αC-helix)-Lys95(β3-strand) interaction, thereby biasing the conformational ensemble toward active-like states. This inward orientation of the αC-helix, directed toward both the ATP-binding pocket and the catalytic center, further positions Lys109 to maintain a persistent and energetically favorable salt bridge with ATP, consistent with enhanced ATP affinity. Consistent with these atomistic observations, PCA and MM/GBSA analyses reveal a phosphorylation-induced population shift toward a lower free-energy ensemble and substantially stronger ATP binding, jointly indicating a coordinated allosteric enhancement of catalytic activity. Further QM/MM MD simulations indicate that T221 phosphorylation pre-organizes the SIK3 active site to position HDAC4-Ser245(Oγ) closer to the ATP γ-phosphate in a reaction-competent arrangement, thereby facilitating Ser245-O-P phosphoester bond formation and promoting Ser245 phosphorylation. Taken together, these findings define-at atomic resolution-the detailed structural and dynamic principles by which T221 phosphorylation regulates SIK3 function, thus providing mechanistic insight into sleep-need homeostasis and offering a foundation for structure-guided development of SIK3-targeted cancer therapeutics.