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Daily Signal April 01, 2026 · 8 min read

Issue #80: A Mini Review on Metal Complexes as Potential Anti-SARS-CoV-2 Agents: Insights from Molecular Docking Studies.

Protein Design Digest #80: Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of L…

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A Mini Review on Metal Complexes as Potential Anti-SARS-CoV-2 Agents: Insights from Molecular Docking Studies.

There is an urgent need to develop effective antiviral treatments against SARS-CoV-2. Despite the availability of vaccines, drug discovery remains critical for combating emerging variants. Molecular docking studies have become a vital computational tool for identifying antiviral drugs capable of inhibiting different SARS-CoV-2 proteins. This review explores the role of metal complexes as promising viral inhibitors through in silico molecular docking approaches. The binding abilities of several coordination complexes derived from iron, copper, palladium, and zinc ions have been evaluated against major viral proteins such as the spike glycoprotein, RNA-dependent RNA polymerase (RdRp), and the main protease (Mpro), which are responsible for viral infection. Comparative docking studies of specific metal-based compounds with conventional antiviral drugs highlight their superior binding affinities and inhibitory potential. Furthermore, ADME (Absorption, Distribution, Metabolism, and Excretion) analyses, molecular dynamics simulations, and drugdelivery strategies are discussed to assess pharmacokinetics and therapeutic viability. Overall, this review emphasizes the importance of molecular docking in the rational design of metal complexes as antiviral agents and its relevance for developing effective therapeutic strategies to combat COVID-19.

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Also Worth Reading

Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors of HER2 L755S, T798I, and T798M based on a Large Database of Curcumin Derivatives.

Objective This study presents a methodology employing virtual screening to identify curcumin derivatives with selective affinity for the HER2 mutations L755S, T798I, and T798M. Methods Curcumin derivatives were retrieved from the ChEMBL database and filtered using KNIME. HER2 mutations were modeled in silico using MOE software with PDB ID 3RCD. Molecular docking and dynamics simulations were conducted to screen high-affinity compounds and evaluate binding interactions. Result From 505 curcumin derivatives, the RDKit module implemented in KNIME successfully filtered 317 compounds. Subsequent molecular docking against wild-type HER2 identified 100 curcumin derivatives with low docking scores, among which the top 20 compounds exhibited better binding affinities than Lapatinib. Further molecular docking screening against the three HER2 mutations identified five lead compounds with the lowest docking scores. Molecular docking and molecular dynamics simulation revealed critical binding interactions with residues essential for kinase domain stability. Chemical structural analysis revealed key modifications, such as geranyl and tripeptide modifications. CHEMBL3758656 and CHEMBL3827366, two curcumin derivatives, demonstrated consistent binding across HER2 mutations and a favorable ADMET profile. Conclusion This study successfully identified CHEMBL3758656 and CHEMBL3827366 as promising HER2 inhibitors through comprehensive virtual screening. Their high binding affinity against L755S, T798I, and T798M mutations and favorable ADME and toxicity properties underscore their potential as alternative therapeutics for HER2-positive breast cancer.

Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards

Generative modeling has become a central paradigm in protein research, extending machine learning beyond structure prediction toward sequence design, backbone generation, inverse folding, and biomolecular interaction modeling. However, the literature remains fragmented across representations, model classes, and task formulations, making it difficult to compare methods or identify appropriate evaluation standards. This survey provides a systematic synthesis of generative AI in protein research, organized around (i) foundational representations spanning sequence, geometric, and multimodal encodings; (ii) generative architectures including $\mathrm{SE}(3)$-equivariant diffusion, flow matching, and hybrid predictor-generator systems; and (iii) task settings from structure prediction and de novo design to protein-ligand and protein-protein interactions. Beyond cataloging methods, we compare assumptions, conditioning mechanisms, and controllability, and we synthesize evaluation best practices that emphasize leakage-aware splits, physical validity checks, and function-oriented benchmarks. We conclude with critical open challenges: modeling conformational dynamics and intrinsically disordered regions, scaling to large assemblies while maintaining efficiency, and developing robust safety frameworks for dual-use biosecurity risks. By unifying architectural advances with practical evaluation standards and responsible development considerations, this survey aims to accelerate the transition from predictive modeling to reliable, function-driven protein engineering.

Innovative integration of molecular docking and machine learning for drug discovery: from virtual screening to nanomolar inhibitors.

This review highlights our group’s systematic approach to integrating molecular docking, pharmacophore modeling, and machine learning methodologies for the rational discovery of bioactive leads. We describe innovative strategies including docking-based data augmentation, ligand-receptor contact fingerprints, genetic algorithm-guided feature selection, and SHAP-based model interpretation. These approaches have enabled the discovery of nanomolar inhibitors against multiple therapeutic targets including STAT3, TTK, LSD-1, and HER2. The presented workflow demonstrates how machine learning (ML) can be synergistically combined with traditional computer-aided drug design methods to achieve efficient scaffold hopping and identify novel chemotypes with potent biological activities.

Research & AI Updates

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Investigation of in vitro anticancer and antioxidant activities of various extracts of Bayramiç Beyazı nectarine, and molecular docking, molecular dynamics simulation, and protein-protein interaction network

Nectarine (Prunus persica var. Read more →

Triazinyl-benzenesulfonamide derivatives as hCA IX inhibitors: Design, synthesis, and activity determination using an optimized stop-flow methodology.

This study presents the design, synthesis, and biological evaluation of a new series of 1,3,5-triazinyl benzenesulfonamide derivatives incorporating substituted piperazines, aminobenzenes, or adamantane moieties. Read more →

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.

Background Heart failure is a leading global health burden, often driven by Angiotensin II (Ang II)-induced processes such as inflammation, fibrosis, and extracellular matrix remodeling. Read more →

Machine Learning–Guided QSAR Screening of Fluconazole Analogs and FDA-Approved Drugs against Candida albicans, with Docking, Molecular Dynamics, and ADMET Analysis

Abstract Infections caused by Candida albicans represent an increasing public health concern, with the pathogen classified by the WHO as critically important. Read more →

A Mini Review on Metal Complexes as Potential Anti-SARS-CoV-2 Agents: Insights from Molecular Docking Studies.

There is an urgent need to develop effective antiviral treatments against SARS-CoV-2. Read more →

Bisphenol a exposure and major depressive disorder: an integrative analysis combining network toxicology, molecular docking, genetic epidemiology, and transcriptomic validation.

Bisphenol A (BPA) is a widely used endocrine-disrupting chemical that has been implicated in neurodevelopmental and psychiatric disorders; however, the molecular mechanisms linking BPA exposure to major depressive disorder (MDD) remain poorly understood. Read more →

Synthesis, antimicrobial evaluation, molecular docking, and drug-likeness assessment of novel phenothiazine chromene hybrid compounds.

By carefully hybridizing with scaffolds generated from chromene, pyranochromene, and cyanoacetamide, a novel class of phenothiazine-based heterocycles was created. Read more →

A chimeric natriuretic peptide (Ev-NP) inhibits isoproterenol (ISO)-induced hypertrophic growth in in vivo and in vitro models by enhancing cGMP and its downstream signaling targets: in silico docking and binding efficacy analysis with NPR-A and NPR-B receptors.

A chimeric natriuretic peptide (Ev-NP) was engineered and created with an intention of having a dual NPR-A/NPR-B activation, resistance to degradation, and with a strong renal, and anti-hypertrophic actions in the heart. Read more →

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Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman

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