Search Results (286)

Drug discovery is a complex and expensive process in which only a small proportion of candidate molecules reach clinical approval. Computational methods, particularly computer-aided drug design (CADD), have become fundamental to accelerate and optimize early stages of discovery by integrating chemical, biological, and pharmacokinetic information into predictive models. This review outlines a complete computational workflow for chemical compound analysis, covering molecular structure generation, database selection, evaluation of absorption, distribution, metabolism, excretion and toxicity (ADMET), target prediction, and molecular docking. It focuses on freely accessible and web-based tools that enable reproducible, cost-effective, and scalable in silico studies. Key platforms such as PubChem, ChEMBL, RDKit, SwissADME, TargetNet, and SwissDock are highlighted as examples of how different resources can be integrated to support rational compound design and prioritization. The article also discusses essential methodological principles, data curation strategies, and common limitations in virtual screening and docking analyses. Finally, it explores future directions in computational drug discovery, including the incorporation of artificial intelligence, multi-omics integration, and quantum simulations, to enhance predictive accuracy and translational relevance. Full article

Background/Objectives: Diabetes mellitus is a serious global disease characterized by chronic hyperglycemia, resulting from defects in insulin secretion, insulin action, or both. It represents a major health concern affecting millions of people worldwide. This condition can lead to severe complications significantly affecting patients’ quality of life. Due to the limitations and side effects of current therapies, the search for safer and more effective antidiabetic agents, particularly from natural sources, has gained considerable attention. This study investigates the antidiabetic potential of seaweed-derived compounds through structure-based virtual screening targeting α-glucosidase. Methods: A library of compounds derived from the Seaweed Metabolite Database was subjected to a hierarchical molecular docking protocol against α-glucosidase. Extra Precision (XP) docking was employed to identify the top-ranked ligands based on their binding affinities. Drug-likeness was assessed according to Lipinski’s Rule of Five, followed by pharmacokinetic and toxicity predictions to evaluate ADMET properties. Density Functional Theory (DFT) calculations were performed to analyze the electronic properties and chemical reactivity of the selected compounds. Furthermore, molecular dynamics simulations were carried out to examine the stability and dynamic behavior of the ligand–enzyme complexes. Results: Following XP docking and ADMET prediction, four promising compounds were selected: Colensolide A, Rhodomelol, Callophycin A, and 7-(2,3-dibromo-4,5-dihydroxybenzyl)-3,7-dihydro-1H-purine-2,6-dione. Molecular dynamics simulations further confirmed the structural stability and strong binding interactions of these compounds within the α-glucosidase active site. Conclusions: This investigation demonstrated the important role of seaweed-derived compounds in inhibiting α-glucosidase activity. Further experimental validation is warranted to confirm their biological activity and therapeutic potential. Full article

The development of KRAS G12D inhibitors represents an effective therapeutic strategy for treating oncological pathologies. Existing quantitative structure-activity relationship (QSAR) models for KRAS G12D inhibitors have several limitations, primarily the lack of applicability domain determination and virtual screening implementation. In this study, we propose a set of regression QSAR models for KRAS G12D inhibitors by employing various molecular descriptors and machine learning methods. Our consensus model achieved a Q² test value of 0.70 on an external test set, covering 78% of the data within the applicability domain. We integrated this consensus model into our Python-based framework KRASAVA. The platform predicts inhibitory activity while considering the applicability domain, assesses compounds for compliance with Muegge’s bioavailability rules, and identifies PAINS, toxicophores, and Brenk filters. Furthermore, we structurally interpreted the QSAR models to propose several promising inhibitors and performed molecular docking on these candidates using GNINA. For the reference inhibitor MRTX1133, we reproduced the crystal structure pose with an RMSD of 0.76 Å (PDB ID: 7T47). The key interactions with amino acid residues Asp12, Asp69, His95, Arg68, and Gly60, identified for both MRTX1133 and our proposed compounds, demonstrate a strong consistency between the molecular docking and QSAR results. Full article

Background: Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor worldwide and is associated with a poor prognosis. Oxidative stress is a key factor in the occurrence and progression of HCC. KHK-A, a key protein in the oxidative stress pathway, plays an important role in various cancers. This study aimed to discover small-molecule inhibitors targeting KHK-A through structure-based virtual screening, evaluate their therapeutic effects on HCC, and explore the potential of KHK-A as a therapeutic target for HCC. Methods: Based on the crystal structure of KHK-A, potential small-molecule inhibitors (HK1 to HK-24) were screened from the SPECS database using the Discovery Studio (DS) 2019 software. The effects of these compounds were evaluated through molecular docking and cellular experiments. Results: The screened compound HK-4 significantly inhibited HCC cell proliferation, migration, and invasion ex vivo. The half-maximal inhibitory concentrations (IC₅₀) of HK-4 in HepG2, PLC/PRF/5, and HuH7 cells were 22.54 µM, 23.91 µM, and 23.38 µM, respectively. HK-4 induced G1 phase arrest and apoptosis, and reduced the protein levels of p-AKT and p-mTOR in the PI3K-AKT signaling pathway. Conclusions: Through structure-based virtual screening, this study identified HK-4, a small-molecule inhibitor of KHK-A with anti-HCC activity. Its mechanism of action is closely related to the regulation of the PI3K-AKT signaling pathway. This finding provides experimental evidence supporting KHK-A as a therapeutic target for HCC and offers a new direction for the development of novel anti-HCC drugs. Full article

Background/Objectives: GuaB, which is known as inosine 5′-phosphate dehydrogenase (IMPDH), is an enzymatic target involved in the de novo guanine biosynthetic pathway of the multidrug-resistant (MDR) Acinetobacter baumannii. GuaB has emerged as a potential therapeutic target to cope with increasing antibiotic resistance. Here, we used machine learning-based virtual screening as a verification technique to find potential inhibitors possessing different chemical scaffolds, using structure-based drug design as a discovery platform. Methods: Four machine learning models, built based on chemical fingerprint data, were trained, and the best models were used for virtual screening of the ChEMBL library, which covers 153 active molecules. Molecular dynamics (MD) simulations of 200 ns were carried out for all three compounds in order to explain conformational changes, evaluate stability, and provide validation of the docking results. Post-simulation analyses include principal component analysis (PCA), bond analysis, free-energy landscape (FEL), dynamic cross-correlation matrix (DCCM), radial distribution function (RDF), salt-bridge identification, and secondary-structure profiling, etc. Results: For molecular docking, the screened compounds were used against the GuaB protein to achieve proper docked conformation. Upon visual examination of the best-docked compounds, three leads (lead-1, lead-2, and lead-3) were found to have better interaction with the GuaB protein in comparison to the control. The mean RMSD scores between the three leads and the control were between 2.54 and 2.89 Å. In addition, the three leads as well as the control were characterized for pharmacokinetic features. All three leads met Lipinski’s Rule 5 and were thus drug-like. PCA and FEL analyses showed that lead-2 exhibited improved conformational stability, identified as deeper energy minima, whereas RDF and DCCM analyses revealed that lead-2 and lead-3 exhibited strong local structuring and concerted dynamics. In addition, lead-2 displayed a very rich hydrogen-bonding network with a total of 460 frames possessing such interactions, which is the highest among the complexes investigated here. Based on entropy calculations and the maximum entropy method of gamma–gram, lead-1 proved to be the most stable one with the lowest binding free-energy. Conclusions: This study provides an integrated machine learning-based virtual screening pipeline for the identification of new scaffolds to moderate infections associated with AMR; however, in vitro validation is still required to assess the efficacy of such compounds. Full article

Serotonin receptors, in particular 5-HT_1A and 5-HT_2A receptors, are important molecular targets for the central nervous system (CNS) disorders, such as schizophrenia, depression, anxiety disorders, memory deficits, and many others. Here, we present structural and pharmacological evaluation of a serotonin receptor ligand, SERAAK2, identified in a structure-based virtual screening campaign. Molecular docking studies revealed that SERAAK2 binds with its molecular targets via Asp3.32 as the main anchoring point, which is typical for orthosteric ligands of aminergic GPCRs. Molecular dynamics simulations confirmed the stability of the ligand binding poses in the studied receptors. MMGBSA calculations were in accordance with the receptor in vitro binding affinity studies, which indicated that SERAAK2 is a potent ligand of 5-HT_1A and 5-HT_2A receptors. It was also found that SERAAK2 displays favorable ADMET parameters. The demonstrated anxiolytic- and antidepressant-like effects of SERAAK2 in animal models, which may involve its interaction with 5-HT_1A receptors, warrant further studies to confirm these activities and elucidate the underlying mechanisms. Full article

Aberrant activation of fibroblast growth factor receptor 1 (FGFR1) drives tumor progression in multiple cancer types, yet existing FGFR1 inhibitors suffer from suboptimal target selectivity and dose-limiting toxicities. This study describes an integrated computational approach for the identification of novel FGFR1 inhibitors. We established a computational pipeline incorporating ligand-based pharmacophore modeling, multi-tiered virtual screening with hierarchical docking (HTVS/SP/XP), and MM-GBSA binding energy calculations to evaluate interactions within the FGFR1 kinase domain. From an initial library of 9019 anticancer compounds, three hit compounds exhibited superior FGFR1 binding affinity compared to the reference ligand 4UT801. Scaffold hopping was performed to generate 5355 structural derivatives, among which candidate compounds 20357a–20357c showed improved bioavailability and reduced toxicity as predicted by absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling. Molecular dynamics (MD) simulations validated stable binding modes and favorable interaction energies for these candidates. Collectively, our study identifies structurally novel FGFR1 inhibitors with optimized pharmacodynamic and safety profiles, thereby advancing targeted anticancer drug discovery. Full article

Protein–protein interactions (PPIs) take place in many cellular processes, including the activation of cellular cascades, such as the MAPK/ERK (Mitogen-Activated Protein Kinase/Extracellular-Regulated Kinase) pathway. Deregulation of these pathways leads to the development of diseases, such as cancer. DEL-22379 is an ERK2 dimerization inhibitor, which presents anti-tumoral effects, without affecting ERK2 phosphorylation. Our aim was to identify new therapeutic molecules targeting ERK2 dimerization, based on DEL-22379 structure. In this study, we implemented a combination of computational and experimental workflow, which includes in silico techniques, such as scaffold hopping and virtual screening to generate a dataset of candidate compounds, a native PAGE (PolyAcrylamide Gel Electrophoresis) electrophoresis to experimentally screen the potential inhibitors, and a detailed molecular docking and chemical profile prediction to understand the potential mechanism of action of the selected compounds. From an initial dataset of 536 compounds, we obtained two hit molecules that exhibited inhibitory effects on ERK2 dimerization: Drug73 and Drug120. A computational analysis of the mechanism of action, unveiled that Drug73 and Drug120 presented an improved docking score, and better drug-like properties when compared to DEL-22379. This study shows that computational studies, in combination with experimental evaluation, can be useful and efficient to find new therapeutic compounds. Full article

Background: Disability-linked neurodegeneration involves cholinergic dysfunction, amyloidogenesis, glutamatergic excitotoxicity, and dopaminergic imbalance, highlighting the need for multi-target modulation. Catharanthus roseus contains a diverse array of metabolites with potential polypharmacological properties. Methods: We curated 318 Catharanthus roseus metabolites and performed structure-based virtual screening against five CNS targets, namely BACE1, AChE, MAO-B, NMDAR, and D1, using target-specific positive controls. Cross-target intersection ranking nominated three hits. We assessed dynamic stability by 200 ns all-atom molecular dynamics simulations (MDS) and MM/PBSA; ADMET-AI profiled CNS-relevant properties. Results: The three metabolites (PubChem CIDs 485711, 56964592, and 162963996) repeatedly ranked among top binders across targets. All five protein–ligand complexes reached stable MD plateaus (RMSD < ~0.30 nm) with sustained key interactions; BACE1 and AChE showed the highest contact persistence and most favorable ΔG_total/ligand-efficiency. Conclusions: Convergent docking, MDS, and MM/PBSA support these metabolites as tractable multi-target leads, with BACE1/AChE prioritized for enzyme-level validation and the remaining targets for follow-up studies. Full article

CRISPR-Cas systems have transformed genome engineering with their exceptional precision, programmability, and affordability. Although they originate from microbial defense mechanisms, expanding their use, especially in therapeutics, requires a chemically oriented framework that allows for tunable, reversible, and safe gene editing. This review offers a multidisciplinary look at recent progress in the structural, synthetic, and computational aspects of CRISPR-Cas technologies. Structural analyses examine the domain architectures of Cas enzymes, including the recognition (REC), nuclease (HNH and RuvC), and PAM-interacting domains, emphasizing the catalytic importance of divalent metal ions. Comparative insights into Cas9, Cas12, and Cas13 demonstrate functional diversity across DNA- and RNA-targeting systems, supported by high-resolution structural data on guide RNA pairing and conformational dynamics. The review highlights advances in chemical modulation, such as anti-CRISPR proteins, small-molecule inhibitors, and stimuli-responsive switches, focusing on structure–activity relationships. Additionally, bioorganic delivery systems like lipid nanoparticles, polymers, and cell-penetrating peptides are discussed for their role in improving in vivo delivery through formulation chemistry. Computational chemistry methods—molecular docking, molecular dynamics simulations, and virtual screening—are identified as critical tools for discovering and optimizing modulators. The use of AI-driven tools is proposed as a promising direction for rational CRISPR design. Overall, this chemistry-focused perspective emphasizes the importance of molecular control in developing the next generation of programmable and safe CRISPR-based therapies. Full article

Odorant receptors (ORs) in Drosophila melanogaster represent important proteins of the insect’s olfactory system, enabling the detection of environmental cues such as food sources, host plants, and mating signals. Their modulation by natural ligands offers a sustainable strategy for pest management, particularly through the use of bioactive compounds obtained from agricultural crop and food production residues (ACFPR). In this study, as a model we employed the AlphaFold-predicted structure of the odorant receptor Q9W1P8 for structure-based virtual screening. Molecular docking was carried out using GNINA, a deep learning-enhanced docking tool. Screening of 164 ACFPR-derived compounds from different sources revealed several strong binders, including α-tomatine, peonidin 3-rutinoside, and cinnamtannin B1. Predicted binding modes support the role of plant-derived molecules as candidate modulators of insect olfactory receptors. These findings highlight the utility of integrating AlphaFold models with advanced docking platforms to support the development of sustainable pest management strategies. Full article

Nowadays, the explosive growth of knowledge in the epigenetics field has highlighted DNA methyltransferase 1 (DNMT1) as a key regulator of genomic methylation patterns and a promising therapeutic target in several diseases. In light of the increasing clinical interest in epigenetic enzymes, the present study aimed to develop a robust computational framework for the discovery of novel DNMT1 inhibitors, merging both structure and data-driven strategies. Particularly, the study compiled a dataset of established DNMT1 inhibitors and calculated a series of molecular properties, thus enabling the training of a machine learning model to capture critical structure–activity relationships (SARs). When benchmarked against known active compounds, the model effectively discriminated between putative inhibitors and non-inhibitors with high accuracy. In parallel, molecular docking was conducted to screen additional uncharacterized compounds, estimating their binding affinity to human DNMT1. Their respective properties were then extracted and fed into the aforementioned model to predict their inhibitory potential. Our comparative evaluation against known human DNMT1 inhibitors demonstrated high predictive accuracy, confirming the reliability of the proposed integrated approach. By uniting molecular docking with data-driven SAR modelling, this workflow offers an expedited fast-track avenue for identifying promising human DNMT1 inhibitors while reducing experimental overhead. The results highlight the effectiveness of combining cheminformatics, machine learning, and in silico techniques to guide rational drug design, and accelerate the discovery of novel epigenetic inhibitors. Full article

Poly (ADP-ribose) polymerase 1 (PARP1) is an important enzyme that plays a central role in the DNA damage response, facilitating repair of single-stranded DNA breaks via the base excision repair (BER) pathway and thus genomic integrity. Its therapeutic relevance is compounded in breast cancer, particularly in BRCA1 or BRCA2 mutant cancers, where compromised homologous recombination repair (HRR) leaves a synthetic lethal dependency on PARP1-mediated repair. This review comprehensively discusses the recent advances in computational chemistry for the discovery of PARP1 inhibitors, focusing on their application in breast cancer therapy. Techniques such as molecular docking, molecular dynamics (MD) simulations, quantitative structure–activity relationship (QSAR) modeling, density functional theory (DFT), time-dependent DFT (TD-DFT), and machine learning (ML)-aided virtual screening have revolutionized the discovery of inhibitors. Some of the most prominent examples are Olaparib (IC₅₀ = 5 nM), Rucaparib (IC₅₀ = 7 nM), and Talazoparib (IC₅₀ = 1 nM), which were optimized with docking scores between −9.0 to −9.3 kcal/mol and validated by in vitro and in vivo assays, achieving 60–80% inhibition of tumor growth in BRCA-mutated models and achieving up to 21-month improvement in progression-free survival in clinical trials of BRCA-mutated breast and ovarian cancer patients. These strategies enable site-specific hopping into the PARP1 nicotinamide-binding pocket to enhance inhibitor affinity and specificity and reduce off-target activity. Employing computation and experimental verification in a hybrid strategy have brought next-generation inhibitors to the clinic with accelerated development, higher efficacy, and personalized treatment for breast cancer patients. Future approaches, including AI-aided generative models and multi-omics integration, have the promise to further refine inhibitor design, paving the way for precision oncology. Full article

Background: Virtual screening is a widely adopted technique for the discovery of novel pharmacologically active compounds; however, the risk of identifying false positive hits remains a major challenge. Aim: The aim of this study was to perform a validated structure-based drug design screening to discover multitarget pyrrole-based molecules as selective dual-acting monoamine oxidase (MAO) and acetylcholinesterase (AChE) inhibitors. Methods: The study employed validated docking protocols using Glide (Schrödinger) and GOLD (CCDC), integrating ligand enrichment analysis and robust Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) rescoring. These methods were applied to a custom-designed database of pyrrole-based compounds. The top-ranked hits were synthesized and validated through in vitro tests, demonstrating significant inhibitory activities against MAO-A, MAO-B, AChE, and Butyrylcholinesterase (BChE). Results: The docking protocols achieved favorable hit rates, with 25.93% for AChE inhibitors and 44.44% for MAO-B inhibitors. Additionally, structure–activity relationship analysis revealed key substituent effects that significantly influence binding affinity and selectivity. Two compounds, EM-DC-19 (2-(2,5-dimethyl-1H-pyrrol-1-yl)-3-(2H-imidazol-4-yl)propanoic acid) and EM-DC-27 ([4-(2,5-dimethyl-1H-pyrrol-1-yl)phenyl]acetic acid), were identified as selective MAO-B inhibitors with additional moderate AChE inhibitory activity, demonstrating IC₅₀ values of 0.299 ± 0.10 µM and 0.344 ± 0.10 µM against MAO-B, and 76.15 ± 6.12 µM and 375.20 ± 52.99 µM against AChE, respectively. The absence of statistically significant inhibitory effects of these lead compounds on MAO-A and BChE (IC₅₀ > 100 µM) underscores their selective inhibitory activity towards MAO-B and AChE. Furthermore, both compounds demonstrated low neurotoxicity and significant neuroprotective and antioxidant effects in rat brain synaptosomes, mitochondria, and microsomes. These effects were particularly evident in models of 6-hydroxydopamine-induced neurotoxicity (6-OHDA) and oxidative stress induced by tert-butyl hydroperoxide and Fe²⁺/ascorbic acid. Conclusions: The findings suggest that these multitarget compounds hold promise for further development, with potential for structural modifications to enhance their enzyme inhibitory and neuroprotective properties. Full article

Background/Objectives: Janus kinase 2 (JAK2) is a pivotal signaling protein implicated in various hematological malignancies and inflammatory disorders, making it a compelling target for therapeutic intervention. Methods: In this study, we employed an integrative computational approach combining ligand-based screening, pharmacophore modeling, molecular docking, molecular dynamics (MD) simulations, and MM/PBSA free energy calculations to identify JAK2 inhibitors from the ChEMBL database. A comprehensive virtual screening of over 1,900,000 compounds was conducted using Tanimoto similarity and a validated pharmacophore model, resulting in the identification of 39 structurally promising candidates. Docking analyses prioritized compounds with favorable interaction energies, while MD simulations over 100 ns assessed the dynamic behavior and binding stability of top hits. Results: Four compounds, CHEMBL4169802, CHEMBL4162254, CHEMBL4286867, and CHEMBL2208033, exhibited consistently superior performance, forming stable hydrogen bonds, favorable RMSD profiles (≤0.5 nm), and strong binding interactions, including salt bridges. Notably, the binding free energies revealed ΔG values as low as −29.91 kcal/mol, surpassing that of the reference inhibitor, momelotinib (−24.17 kcal/mol). Conclusions: Among these, CHEMBL4169802 emerged as the most promising candidate due to its synergistic electrostatic and hydrophobic interactions. Collectively, our results highlight these compounds as probable, JAK2-selective inhibitors with strong potential for further biological validation and optimization. Full article