The rapid emergence of metallo-b-lactamase-mediated antibiotic resistance has created an urgent need for new inhibitor discovery strategies. In this work, a machine-learning-guided workflow was developed to generate and prioritize potential inhibitors targeting NDM-1. A SMILES-based variational autoencoder was first pretrained on a broad molecular dataset to learn general chemical syntax and latent molecular representations. The model was then fine-tuned on an 8-hydroxyquinoline-enriched dataset to bias molecular generation toward zinc-binding chemical space relevant to metallo-β-lactamase inhibition. Generated compounds were processed through structural filtering and docking-based evaluation to create training data for downstream predictive modeling. Molecular fingerprints and physicochemical descriptors were then used to train XGBoost models for docking score prediction and classification of potential binders. Classification proved especially useful for prescreening because it avoided overinterpreting small differences in noisy docking scores while still enriching for compounds likely to perform well in docking. The resulting workflow demonstrates how generative modeling and supervised machine learning can be combined to reduce chemical search space, prioritize candidate inhibitors, and guide computational drug discovery. Although experimental validation remains necessary, this approach provides a scalable framework for identifying promising zinc-binding compounds for further molecular simulation and inhibitor development that can be expanded in future studies.
Findings highlight Z4P as a promising mutation-resilient IRE1 inhibitor and validate the effectiveness of the integrated computational pipeline for identifying potential anti-cancer therapeutics.
Nithisha L Bastin, P. K. Praveen Kumar, B. Ethiraj et al.· Scientific Reports· 0 citations
The proposed workflow efficiently reduced a large chemical space to a focused set of TNKS1 inhibitor candidates while substantially reducing the experimental screening burden, highlighting the value of integrating consensus ML, SBVS, and experimental validation to accelerate early-stage hit discovery for TNKS1 and other therapeutic targets.
M. Bilotta, Adriana Gargano, R. Rocca et al.· Pharmaceuticals· 0 citations
An integrated computer-aided drug design (CADD) and artificial intelligence (AI) framework to systematically identify selective ALDH1A1 inhibitors from a heterocyclic compound library is developed and suggests that LDN-27219 exhibits favorable binding characteristics and represents a promising lead candidate for subsequent experimental validation.
A structure-based virtual screening workflow was implemented to optimize the known KDM4A inhibitor QC6352 and design novel isonicotinic acid derivatives with improved predicted binding affinity and dynamic stability and introduces promising scaffolds for KDM4A-targeted drug design.
Kourosh Eslami, Hajar Sirous, Ilaria Cursaro et al.· Journal of Computer-Aided Mo...· 1 citation
These findings introduce H_1 as a computationally prioritized, putative MLK4-binding lead and provide a hypothesis-generating framework for MLK4-targeted scaffold prioritization, while recognizing that experimental activity and kinome selectivity profiling remain necessary before H_1 can be described as a confirmed MLK4 inhibitor or MLK4-selective compound.
Afnan A. Alzaghari, S. Daoud, Husam Nassar et al.· Journal of Pharmaceutical In...· 0 citations
Interferon-inducible RNA-dependent protein kinase (PKR) is an emerging therapeutic target involved in cancer, neurodegeneration, and inflammatory disorders; however, the discovery of potent and structurally diverse PKR inhibitors remains limited. In this study, we report an integrated computational–experimental strategy for the identification of novel PKR inhibitory chemotypes. Pharmacophore models were generated from flexible docking of structurally diverse reference inhibitors and validated using receiver operating characteristic (ROC) analysis. To better capture ligand flexibility and address data scarcity, multiple conformations of 223 PKR inhibitors were employed as a data augmentation strategy in machine learning-based QSAR modelling. Among several algorithms evaluated, a Naïve Bayes classifier combined with genetic function algorithm (GFA) feature selection provided the most predictive model. The optimized model, incorporating a single pharmacophore hypothesis and key physicochemical descriptors, was applied to virtual screening of the Boehringer Ingelheim opnMe compound library. Experimental validation using a PKR kinase assay identified the pre-synthesized compound BI-8128 as a potent PKR inhibitor, with an IC50 value of 174.8 nM, demonstrating higher potency than the reference inhibitor C16 under identical conditions. Notably, this compound represents a structurally distinct scaffold compared to reported PKR inhibitors, indicating effective scaffold hopping. To the best of our knowledge, this is the first report of PKR inhibitory activity for BI-8128. Overall, this work demonstrates that integrating docking-derived pharmacophores with conformational ensemble-based machine learning provides an effective approach for discovering novel inhibitors against underexplored kinase targets.
G. Shakhatreh, M. Taha, S. Daoud· RSC Advances· 0 citations