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.

AI-fragmented derivatives of methotrexate to design effective and safer DHFR inhibitor: A computational breakthrough for ectopic pregnancy therapy.

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.

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.

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.

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

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

Computational insights into the allosteric modulation of E. coli type 1 fimbriae by curcumin: FimH, FimG, FimF, FimA, FimD through docking, dynamics, DFT, MMGBSA analysis.

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.

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.

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder characterized by cognitive decline and memory loss. Current treatments offer limited efficacy, necessitating the development of innovative multitarget therapeutic strategies. Here, we present N3,N5-bis(2-(5-methoxy-1H-indol-3-yl)ethyl)-2,6-dimethyl-4-(2-nitrophenyl)pyridine-3,5-dicarboxamide (HCM-01), a novel compound developed to target multiple neurodegenerative pathways implicated in AD. In vitro assays included MTT-based cell viability analyses performed in two complementary experimental settings: primary neuronal cultures and astrocyte-based in vitro cell culture models exposed to glutamate. In primary hippocampal neuronal cultures, glutamate exposure induced a statistically significant reduction in cell viability compared with vehicle-treated controls, consistent with glutamate-induced excitotoxicity. Under these conditions, HCM-01 treatment resulted in a statistically significant improvement in neuronal viability, showing a greater protective effect compared with donepezil and memantine. In contrast, in astrocyte-based in vitro cultures, the applied glutamate concentration did not induce overt cytotoxicity, in line with the intrinsic neuroprotective and glutamate-buffering role of astrocytes. Accordingly, astrocytic experiments were designed to assess functional modulation of glutamate-handling mechanisms rather than cell survival. Western blot analysis in C8-D1A astrocytic cells demonstrated increased expression of excitatory amino acid transporter 2 (EAAT2) following HCM-01 treatment compared with control and reference drug-treated groups, suggesting modulation of astrocyte-mediated glutamate homeostasis. In parallel, redox analyses revealed that HCM-01 improved oxidative/antioxidative balance, as evidenced by increased total antioxidant capacity (TAC) and reduced total oxidant status (TOS), supporting an indirect antioxidant contribution to its functional effects. In vivo behavioral assessment of HCM-01 in a streptozotocin (STZ)-induced Alzheimer's model in female Sprague-Dawley rats demonstrated that administration of HCM-01 at doses of 50 mg/kg orally (oral, P.O. and intraperitoneal, I.P.) and 100 mg/kg (P.O.), significantly improved cognitive and memory functions in the passive avoidance (PA), Morris water maze (MWM), and locomotor activity tests. Moreover, histopathological and immunohistochemical analyses of different hippocampal regions revealed reduced neuronal damage, attenuation of tau pathology, antiamyloidogenic effect, and restoration of cholinergic function. Complementary in silico studies, including molecular docking, molecular dynamics simulations (MDS), and free energy calculations, suggested potential interactions of HCM-01 with the allosteric site of EAAT2. Taken together, these findings suggest that HCM-01 exerts neuroprotective effects against glutamate-induced excitotoxicity in primary hippocampal neurons while additionally modulating glutamatergic homeostasis and redox balance through functional mechanisms in astrocyte-based models, supporting its relevance as a multitarget preclinical candidate for early stage AD mechanisms.

Polyethylene glycol (PEG) is a nonionic and highly water-soluble polymer, and PEGylation is widely used as a versatile chemical strategy to enhance the water solubility of otherwise poorly soluble molecules. The rational design of PEG-appended materials, therefore, requires an estimation of the PEG chain length necessary to achieve the desired level of solubility. Here, we investigated the relationship between the PEG chain length and the water solubility of PEG-appended molecules using a combination of experimental and computational approaches. We focused on monodisperse PEG with a discrete molecular weight, which enables us to identify the relationship between chain length and physicochemical properties, as represented by water solubility. Then, we designed a series of PEG-appended molecules bearing monodisperse PEG chains of varying lengths, and measured their water solubilities experimentally. In parallel, molecular dynamics-based alchemical free energy calculations yielded thermodynamic trends that reproduced the observed dependence of solubility on PEG chain length. Comparison of experimental and computational results showed that the calculations not only distinguished between water-soluble and insoluble PEG-appended molecules but also captured the solubility trends for moderately soluble species. These findings demonstrate the potential of molecular-dynamics-based free energy calculations for a priori prediction of the water solubility of PEG-appended molecules.

We present PDBTools.jl, a lightweight and high-performance Julia package for reading, writing, selecting, and analyzing molecular structure data stored in PDB and mmCIF (PDBx) file formats. The package provides a compact and memory-efficient atom representation based on inline strings and single-precision floating-point coordinates, enabling the handling of very large structures on standard hardware. A flexible and customizable atom selection syntax inspired by VMD is augmented by native support for arbitrary Julia functions as selectors, offering exceptional expressiveness and performance for dynamic queries. Beyond file I/O and selection, PDBTools.jl includes high-performance implementations of key structural analysis algorithms: solvent-accessible surface area (SASA) calculation via the Shrake-Rupley method with Fibonacci lattice sampling, hydrogen bond detection, contact and distance maps, backbone dihedral angles for Ramachandran analysis, secondary structure assignment through integration with STRIDE and DSSP, and protein transfer free energy (m-value) calculations using the Tanford additive model. Cell list-based neighbor finding ensures O(N) scaling for distance-dependent operations with support for periodic boundary conditions. PDBTools.jl is designed for molecular dynamics simulation workflows and integrates with Chemfiles.jl, MolSimToolkit.jl, and ComplexMixtures.jl. The package is freely available under the MIT license from the Julia General Registry, and full documentation can be found at https://m3g.github.io/PDBTools.jl.

FLT3 is a critical therapeutic target for acute myeloid leukemia (AML), and its inhibition remains a key strategy in AML management. In this study, we optimized the known compound CHEMBL4444839 to design a novel analogue, CHEMBL4444839-Analogue, showing improved pharmacological and structural characteristics. Pharmacokinetic profiling evaluation revealed that the analogue had enhanced drug-likeness, metabolic stability, and intestinal permeability, along with reduced predicted toxicity. Molecular docking revealed that CHEMBL4444839-Analogue exhibited a binding energy of -10.4kcal/mol versus -8.7kcal/mol for the parent compound, forming additional hydrogen bonds and hydrophobic contacts with GLU661, CYS694, LEU818, and PHE830 in the ATP binding pocket. Molecular dynamics simulation over 100ns demonstrated lower average RMSD (1.78Å vs 2.34Å) and reduced RMSF fluctuations at the activation loop and DFG-out region, indicating enhanced conformational stability. Free energy calculations confirmed higher thermodynamic stability of the analogue versus CHEMBL4444839. The analogue also restricted domain motion and improved residue correlation, indicating better stabilization of FLT3 in its inactive conformation. Incorporation of a fluorocyclobutane moiety significantly contributed to enhanced rigidity and optimized interaction geometry, collectively establishing CHEMBL4444839-Analogue as a more promising and selective FLT3 inhibitor for AML therapy. Docking of CHEMBL4444839 and its designed analogue was performed to analyze binding interactions and guide structural modifications. Pharmacokinetic and toxicity parameters were predicted using SwissADME, ADMETlab 2.0, and ProTox-II. Molecular dynamics simulations were executed with GROMACS 2023 using the CHARMM36 force field to assess conformational stability, followed by MM-PBSA and dynamic cross-correlation analyses. The integrated computational results demonstrated that CHEMBL4444839-Analogue achieved stronger binding affinity, reduced flexibility, and superior stability compared to the parent molecule, validating its potential as an optimized FLT3 inhibitor candidate for further biochemical and therapeutic evaluation.

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

Understanding molybdate interactions with iron oxides is essential for characterizing the geochemical behavior of Mo and for elucidating its geochemical cycle in comparison to tungsten (W). However, the molecular-scale structures and thermodynamic stabilities of Mo surface complexes remain poorly resolved. Here, first-principles molecular dynamics (FPMD) simulations are used to investigate the adsorption of molybdate on the goethite (110) surface. Considering molybdate (MoO42-) and its possible protonation states (HMoO4- and H2MoO4), as well as their different coordination environments, FPMD results show that bidentate corner-sharing complexes remain stable for all species while protonated species spontaneously deprotonate during the simulations. These results indicate that deprotonated states are common under environmentally relevant pH conditions, while protonated species undergo spontaneous deprotonation. Free-energy calculations show that 4-coordinated bidentate complexes are more favored than 5-coordinated bidentate, monodentate, and outer-sphere species. Comparison with our previous study of W adsorption shows that, although Mo and W display comparable desorption free energies, W more readily forms 5-coordinate structures whereas Mo prefers 4-coordinate binding, implying a greater tendency of W for lattice incorporation. These findings provide molecular-level insight into Mo-iron oxide interactions and a mechanistic understanding of Mo and W mobility in natural environments.

Difference spectroscopy between two similar related spectra is routinely applied to highlight features associated with the difference, although computation of such a small difference spectrum by subtracting two spectra is quite challenging due to its huge sampling cost. To overcome this challenge, we have developed a series of new theories that enabled direct calculation of minute difference spectra under background-free conditions, which achieved remarkable improvement of computational efficiency. This work reports our further advances in the theory of difference spectroscopy with the aid of the overlap distribution method of free energy calculations. The present work resolved the main remaining problem of computation in the cases involving different excluded volume regions and thus greatly expanded the applicability of the theoretical analysis to a wide range of systems. We applied the revised theory to the hydrophobic hydration processes and explored spectral features of various association states in the infrared spectrum of water. The present theory paves the way for practical computational analysis of a variety of difference spectroscopic techniques.