Discovery and characterization of xanthine oxidase inhibitory peptides from milk protein hydrolysates: in vitro screening, molecular docking and QSAR modeling

Determination of the binding affinities of OPEs to integrin αvβ3 and elucidation of the underlying mechanisms via a competitive binding assay, pharmacophore modeling, molecular docking and QSAR modeling

As a development from Density Functional Theory, Conceptual Density Functional Theory (CDFT) has emerged as a valuable complementary approach in modern drug discovery. Both global and local chemical reactivity descriptors within the framework of CDFT have made it easier to study chemical reactions and how drugs affect their targets. They aid to predict the electronic properties of drug candidates, which simplifies the process of enhancing important characteristics such as their binding affinity, level of selectivity among others. They help in exploring and analyzing inhibitors that work specifically and allow to predict the negative effects those inhibitors may have. When applied with other approaches such as molecular docking and QSAR modeling, CDFT strengthens the whole drug discovery process. This review highlights the increasing importance of CDFT in rational drug design and especially in context of combined efforts with molecular docking and QSAR modeling. It alsoprovides a fundamental knowledge of CDFT-based descriptors and their role in drug discovery process. Inaddition to highlighting existing challenges, this review outlines potential future directions.

Aim: This work aims to understand potential inhibitory structural requirements and identify lead compounds for non-small cell lung cancer through 3D-QSAR pharmacophore-based virtual screening, molecular docking, CoMSIA and CoMFA QSAR modelling. Materials and Methods: QSAR pharmacophore models were developed by HypoGen Module and validated by test data set, Fischer’s randomization and Guner-Henry equation. The well-validated pharmacophore model was employed to perform virtual screening to identify potent hits from ZINC database. The retrieved hits were subsequently subjected to filtering using ADMET descriptors and Lipinski’s Rule of Five. CoMSIA and CoMFA were then utilized to produce QSAR models on phenylpyrimidine derivatives also. Results: Validations on 3D-QSAR pharmacophore model indicate that the enrichment factor is 6.34, GH is 0.517 and a correlation coefficient is 0.83, implying its highly predictive ability. Top three hits: ZINC29356266, ZINC06589615, and ZINC03375633 were identified as promising potent inhibitory candidates with IC50 value of about 0.54 µM and fitness value of about 59.4. Interestingly, the top three hits indicate dual inhibitory activity targeting EGFR and PD-L1 from structure-based docking. Two developed QSAR models from CoMSIA and CoMFA modelling indicate a potential predictive ability (q2=0.67, and 0.71 respectively). The designed compound C indicates a more potential (dual) inhibitory activity (pIC50=7.39) targeting EGFR (fitness=59.78) and CTLA-4 (fitness =47.90). Conclusion: Validations indicate that the developed 3D-QSAR pharmacophore model is highly predictive. Top three hits were identified as promising potent inhibitory candidates and indicated dual inhibitory activity targeting EGFR and PD-L1. The designed compound C indicates a more potential (dual) inhibitory activity targeting EGFR and CTLA‐4. These important 3D-QSAR and molecular docking bioinformatics results achieved from this work should be valuable in designing more promising potent inhibitory candidates and developing novel lead compounds against advanced NSCLC in future.

In the context of human immunodeficiency virus (HIV) treatment, the emergence of therapeutic failures with existing antiretroviral drugs presents a significant challenge. This study aims to employ advanced molecular modeling techniques to identify potential alternatives to current antiretroviral agents. The study focuses on three essential classes of antiretroviral drugs: nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), and protease inhibitors (PIs). Computational analyses were performed on a database of 3,343,652 chemical molecules to evaluate their binding affinities, pharmacokinetic properties, and interactions with viral reverse transcriptase and protease enzymes. Molecular docking, virtual screening, and 3D pharmacophore modeling were utilized to identify promising candidates. Molecular docking revealed compounds with high binding energies and strong interactions at the active sites of target enzymes. Virtual screening narrowed down potential candidates with favorable pharmacological profiles. 3D pharmacophore modeling identified crucial structural features for effective binding. Overall, two molecules for class 1, 7 molecules for class 2, and 2 molecules for class 3 were selected. These compounds exhibited robust binding affinities, interactions with target enzymes, and improved pharmacokinetic properties, showing promise for more effective HIV treatments in cases of therapeutic failures. The combination of molecular docking, virtual screening, and 3D pharmacophore modeling yielded lead compounds that hold potential for addressing HIV therapeutic failures. Further experimental investigations are essential to validate the efficacy and safety of these compounds, with the ultimate goal of advancing toward clinical applications in HIV management.

Breakthroughs in AI and multi-omics for cancer drug discovery: A review

Using molecular docking between organic chemicals and lipid membrane to revise the well known octanol–water partition coefficient of the mixture

RET receptor tyrosine kinase is crucial for nerve and tissue development but can be an important oncogenic driver. This study focuses on exploring the design principles of potent RET inhibitors through molecular docking and 3D-QSAR modeling of 5,6-fused bicyclic heteroaromatic derivatives. First of all, RET inhibitors of 49 different bicyclic substructures were collected from five different data sources and selected through molecular docking simulations. QSAR models were built from the 3399 conformers of 952 RET inhibitors using the partial least-squares method and statistically evaluated. The optimal QSAR model exhibited high predictive performance, with R 2 (of training data) and Q 2 (of test data) values of 0.801 and 0.794, respectively, effectively predicting known inhibitors. The optimal model was doubly verified by patent-filed RET inhibitors as the out-of-set data to demonstrate acceptable residual analysis results. Moreover, feature importance analysis of the QSAR model outlined the impact of substituent characteristics on the inhibitory activity within the 5,6-fused bicyclic heteroaromatic core structures. Furthermore, the relationship between structure and inhibitory activity was successfully applied to the RET screening of known clinical and nonclinical kinase inhibitors to afford accurate off-target prediction.

Drug discovery using advanced computational biology approaches is an emerging field in medical science and holds the promise towards identification of new drugs. The multidrug resistance in bacterial strains is a matter of serious concern specifically related to the pathogens associated with public health. Numerous strategies have been developed in the recent past to combat the MDR concerns. However, still due to upcoming new evolution mechanisms of bacterial strains, the issue has been addressed only to a limited extent. Pertaining to the limitations of molecular techniques, multiple in silico approaches are in trend with great advancements. This chapter is focused toward the description on several in silico techniques for drug discovery with an idea of target identification, namely, virtual screening, molecular docking, MD simulation, QSAR and pharmacophore modelling. In addition to multi-target identification, the structural genomics has also been illustrated which involves the three-dimensional structure predictions of proteins for better understanding to design drugs against MDR.

Although highly active antiretroviral therapy (HAART) is effective in controlling the progression of AIDS, the emergence of drug-resistant strains increases the difficulty of successful treatment of patients with HIV infection. Increasing numbers of patients are facing the dilemma that comes with the running out of drug combinations for HAART. Computational methods play a key role in anti-HIV drug development. A substantial number of studies have been performed in anti-HIV drug development using various computational methods, such as virtual screening, QSAR, molecular docking, and homology modeling, etc. In this review, we summarize recent advances in the application of computational methods to anti-HIV drug development for five key targets as follows: reverse transcriptase, protease, integrase, CCR5, and CXCR4. We hope that this review will stimulate researchers from multiple disciplines to consider computational methods in the anti-HIV drug development process.

Plant-derived bioactives represent a cornerstone in the continuum from ethnomedicine to modern pharmacotherapeutics, yet their transition to clinical utility remains hindered by low solubility, instability, and poor bioavailability. This comprehensive review delineates a triadic convergence of computational chemistry, receptor profiling and polymeric nanoplatforms as advanced strategies for the rational modulation and targeted delivery of phytoconstituents. Computational chemistry, through molecular docking, molecular dynamics, and QSAR modeling, enables atomic-level prediction of bioactive– receptor interactions, structure–activity optimization, and ADMET-based screening to accelerate lead refinement. Receptor profiling, powered by integrated genomics, proteomics and transcriptomics, elucidates molecular targets, binding kinetics, and signaling networks, providing mechanistic insights into multi-target engagement by complex botanicals. Polymeric nanoplatforms including PLGA, chitosan and PEG-based carriers serve as precision delivery systems, enhancing stability, solubility and pharmacokinetic performance while facilitating ligand-mediated active targeting and stimuli-responsive release. Case studies, such as curcumin analogs (EF24/EF31) engineered for NF-?B inhibition and folate-functionalized PLGA– paclitaxel nanocarriers for receptor-directed chemotherapy, exemplify the translational potential of these integrated methodologies. The synergistic interplay between in silico modeling, molecular validation, and nanoengineering redefines phytochemical drug development, shifting paradigms from empirical screening toward data-driven, receptor-specific and precision-guided therapeutics. Emerging frontiers encompassing AI-assisted molecular prediction, green nanofabrication and microfluidic high-throughput screening further expand this framework toward sustainable, personalized phytomedicine. Collectively, these strategies establish a rationalized blueprint for converting complex natural scaffolds into clinically viable, nanotechnology-enabled phytotherapeutics with superior efficacy, safety and translational promise.

The field of computational medicinal chemistry has undergone significant advancements, transitioning from traditional methodologies to contemporary strategies powered by artificial intelligence, machine learning, and big data. Traditional approaches, such as molecular docking and QSAR modeling, have long been the foundation of drug discovery, offering reliable frameworks for target identification and lead optimization. However, contemporary methodologies, including AI-driven target identification, adaptive virtual screening, and generative models, are reshaping the landscape by increasing efficiency and expanding chemical space exploration. This article provides a comprehensive comparison between these two paradigms, highlighting their respective strengths, limitations, and the potential of their integration. By bridging traditional and contemporary approaches, researchers can establish innovative workflows to accelerate drug discovery, ultimately contributing to the development of safer and more effective therapeutics.

HIV AND COVID-19 are of great concern that the global healthcare system have to be treated with new antiviral drugs for the epidemic. While Protease inhibitors and Remdesivir help to fight HIV and COVID-19 infection, drug resistance and side effects are on the rise. In this work, a hybrid molecule Derivatives was added to indazole and pyrimidine scaffolds. Bounding by molecular docking and QSAR modelling, our compound achieved excellent antiviral binds with HIV-1 protease, reverse transcriptase, SARS-CoV-2 main and RNA-dependent RNA polymerase in contrast to the standard drugs with good ADMET results. Atazanavir (-8.3), darunavir (-8.9) and remdesivir (-9.8) docking scores were well above that of our compounds (M1: -11.0 for COVID-19, -9.6 for HIV; M2: -10.5, -10.1) suggesting the possibility that both drugs show antiviral activity, although of course, further validation will take place in the in vivo and in vitro settings [5, 6].

Background/Objectives: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical research and development is transforming the industry by improving efficiency and effectiveness across drug discovery, development, and healthcare delivery. This review explores the diverse applications of AI and ML, emphasizing their role in predictive modeling, drug repurposing, lead optimization, and clinical trials. Additionally, the review highlights AI’s contributions to regulatory compliance, pharmacovigilance, and personalized medicine while addressing ethical and regulatory considerations. Methods: A comprehensive literature review was conducted to assess the impact of AI and ML in various pharmaceutical domains. Research articles, case studies, and industry reports were analyzed to examine AI-driven advancements in predictive modeling, computational chemistry, clinical trials, drug safety, and supply chain management. Results: AI and ML have demonstrated significant advancements in pharmaceutical research, including improved target identification, accelerated drug discovery through generative models, and enhanced structure-based drug design via molecular docking and QSAR modeling. In clinical trials, AI streamlines patient recruitment, predicts trial outcomes, and enables real-time monitoring. AI-driven predictive maintenance, process optimization, and inventory management have enhanced efficiency in pharmaceutical manufacturing and supply chains. Furthermore, AI has revolutionized personalized medicine by enabling precise treatment strategies through genomic data analysis, biomarker discovery, and AI-driven diagnostics. Conclusions: AI and ML are reshaping pharmaceutical research, offering innovative solutions across drug discovery, regulatory compliance, and patient care. The integration of AI enhances treatment outcomes and operational efficiencies while raising ethical and regulatory challenges that require transparent, accountable applications. Future advancements in AI will rely on collaborative efforts to ensure its responsible implementation, ultimately driving the continued transformation of the pharmaceutical sector.

The development of severe drug resistance caused by the extensive use of anti-HIV agents has resulted in resistance mutation that compromise efficacy of anti-retroviral. We have selected triazolothienopyrimidine derivatives for our study since these derivatives have potentially inhibited HIV-1 replication. The objectives of our study were to identify the important pharmacophoric features and correlate 3D chemical structure of of triazolothienopyrimidines and predict their anti-HIV activity using 2D-, 3D-QSAR, pharmacophore modeling, and docking studies to explore their binding to HIV-1 reverse transcriptase. Partial least square and k-nearest neighbor methodology were used to develop 2D-QSAR and 3D-QSAR models. The statistical significance of developed QSAR models was checked by internal and external validation. The developed 2D-QSAR model indicated that a significance of presence of electron withdrawing groups, number of hydrogen bond acceptor, Z component dipole moment, and the moment of inertia at X axis of the compounds on their anti-HIV potential. 3D-QSAR results indicated the influence of electrostatic and steric field descriptors in the anti-HIV potential of triazolothienopyrimidines. The pharmacophore mapping represented the importance of aromatic and hydrogen bond acceptor features of compounds to interact with the target. Docking studies revealed that the binding of the triazolothienopyrimidines to HIV-1 reverse transcriptase involved extensive hydrophobic and polar interactions. Collectively, the results of QSAR, pharmacophore mapping, and the docking studies provide an insight into the understanding of the relationship between triazolothienopyrimidines and anti-HIV activity, which may assist in designing new triazolothienopyrimidines with enhanced HIV-1 reverse transcriptase inhibitory and anti-HIV activity.

High-throughput computational screening for lead discovery and development.