ARTIFICIAL INTELLIGENCE-ASSISTED DRUG DESIGN COMBINED WITH MOLECULAR DOCKING FOR PRIORITIZATION OF POTENTIAL LEAD MOLECULES ACROSS FIVE THERAPEUTIC TARGETS

Systematic Benchmarking of AI-Based Molecular Generation Models for Structure-Based Drug Design

A state-aware functional classifier (SAFC) is developed that integrates molecular dynamics derived receptor ensembles, ensemble docking and protein ligand interaction graphs that provides dynamics-aware functional activity rankings for generated molecules that were partly complementary to docking, drug-likeness and synthetic accessibility scores.

Structure-Based In Silico Screening of PubChem Compounds for Potential Anti-Inflammatory and Anticancer Targets

Multi-target treatment methods are essential because inflammation and cancer involve intricate, interrelated pathways. To screen thirty different small compounds from PubChem for potential multi-target activity, this study employed a structure-based drug design approach. Their binding affinities for key inflammatory, analgesic and carcinogenic protein targets were evaluated using molecular docking, yielding promising candidates for further experimental validation. A total of eight pharmacologically significant receptors were selected: COX-1 (3KK6), COX-2 (3LN1), MOR (4DKL), EGFR (4HJO), ERα (3ERT), AR (1E3G), HER2 (3PP0) and CDK8 (5F19) targets. Protein structures were prepared using Discovery Studio and molecular docking was performed with AutoDock Vina in PyRx. Standard drugs such as aspirin, tramadol, erlotinib, tamoxifen, bicalutamide, lapatinib and raltitrexed were used for comparative evaluation of docking scores and interaction patterns. Post-docking analysis primarily focused on key amino acid interactions, hydrophobic contacts and hydrogen bonding. Among all tested compounds, C25, was identified as the most potent and effective compound. It exhibited strong binding affinities toward 3LN1 (-10.0 kcal/mol), 3PP0 (-9.6 kcal/mol), 5F19 (-11.9 kcal/mol), 3ERT (-8.6 kcal/mol), 1E3G (-8.8 kcal/mol), 4HJO (-8.9 kcal/mol), surpassing their respective standards. Moreover, Verimol K with 3KK6 (-7.8 kcal/mol) and 8-Deoxylactucin with 4DKL (-7.5kcal/mol) showed notable activity. Overall, the pyrazolin derivative represents a novel multi-target ligand with potential applications as an anti-inflammatory, analgesic, and anti-neoplastic agent. Its strong binding affinity across multiple therapeutic targets highlights its promise as a lead scaffold for future drug development.  Bangladesh Pharmaceutical Journal 29(2): 172-192, 2026 (July)

Emerging Trends in Artificial Intelligence-Driven Drug Discovery: Balancing Chemical Design and Pharmacological Properties for Cancer Therapy.

This review emphasizes the influence of new AI platforms like AlphaFold3, molecular interactions are structurally optimized (MISATO), and ZairaChem on the discovery of oncology drugs and examines how AI reconciles chemical design with pharmacological feasibility.

Integrated CADD, Molecular Docking and ADMET Prediction in Anti-Inflammatory Lead Discovery: A Review

Inflammation involves coordinated activation of vascular, immune, lipid mediator, cytokine, transcriptional and inflammasome pathways. Modern anti-inflammatory lead discovery increasingly uses computer-aided drug design to identify plausible protein-ligand interactions before costly laboratory studies. This review summarizes the rationale for target selection, molecular docking, ADMET prediction, toxicity screening and Quality by Design-based documentation in anti-inflammatory computational pharmacology. The article emphasizes that docking scores are hypothesis-generating outputs and must be interpreted with binding-pose quality, residue relevance, pharmacokinetic feasibility and safety prediction. Selected natural scaffolds such as curcumin, quercetin, luteolin, apigenin, resveratrol, berberine, boswellic acid, andrographolide, withaferin A, gallic acid and ellagic acid are discussed as examples of chemically diverse candidates for pathway-based screening

Open-Sourced In Silico Drug Screening.

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

Drug discovery (DD) is a complex, time-consuming, and resource-intensive process that involves the identification of therapeutic targets, selection of bioactive compounds, and extensive experimental validation. The discovery of promising therapeutic compounds from large libraries of phytochemicals and synthetic molecules remains a major challenge in modern drug development. Screening millions of compounds through conventional experimental approaches requires substantial time, cost, and computational resources. In recent years, in- silico molecular docking has emerged as an important computational approach for predicting interactions between small molecules and target proteins, thereby helping researchers prioritize promising compounds for further investigation. Several molecular docking webservers, including iScreen, SwissDock, CB-Dock2, DockThor, and MTiOpenScreen, have been developed to support virtual screening studies. However, many currently available platforms still face some important limitations. Most existing tools lack integrated repositories of medicinal plant-derived phytochemicals and organism-derived bioactive compounds, automated mapping between plants and their associated phytochemicals, and flexible ligand retrieval using chemical names, SMILES strings, PubChem CIDs, or drug names. In addition, many platforms require extensive manual protein and ligand preparation, provide limited support for AlphaFold-predicted protein structures, and lack efficient large-scale multi-target virtual screening. Most existing docking platforms offer limited support for interactive inspection of docked protein-ligand complexes, often requiring users to download the results and analyse them using external molecular visualization software. To address these limitations, we developed FlexAutoDock, an automated cloud-based molecular docking platform that provides a unified environment for protein-ligand docking and large-scale virtual screening. Unlike existing web servers, FlexAutoDock integrates curated repositories of medicinal plant- derived phytochemicals, organism-derived bioactive compounds, and synthetic compounds from the ZINC database while supporting flexible ligand acquisition through medicinal plant or organism selection, chemical names, SMILES strings, PubChem CIDs, and drug-name queries. The platform further streamlines the docking workflow through automated protein structure retrieval from the Protein Data Bank and AlphaFold databases, receptor and ligand preparation, chain-specific protein selection, blind and site-specific docking, interactive visualization of predicted protein-ligand complexes, and scalable multi-target virtual screening. The resulting platform enables rapid, flexible, and large-scale virtual screening while simplifying the molecular docking workflow, providing researchers with an accessible computational resource for accelerating early-stage drug discovery. FlexAutoDock offers a fast, reliable, and accessible computational platform for molecular docking and virtual screening, freely available to the scientific community at http://103.99.177.82:3000/.