An Integrated Consensus Machine Learning and Structure-Based Workflow for the Discovery of Novel Tankyrase 1 Inhibitors

Discovery of a potent TDP1 inhibitor through machine learning-driven predictive modeling combined with structure-based virtual screening and experimental validation

Tyrosyl-DNA phosphodiesterase I (TDP1) repairs topoisomerase I (TOP1)–mediated DNA damage and is a promising anticancer target, particularly in combination with TOP1 inhibitors. However, the discovery of potent and drug-like TDP1 inhibitors remains challenging due to the limited structural diversity of known active compounds. Here, we developed an integrated computational framework combining machine learning (ML), deep learning (DL), and structure-based docking with experimental validation. A curated dataset of 2040 compounds (857 active, 1183 inactive) was assembled and analyzed by scaffold composition. A total of 40 binary classification models were constructed using six ML algorithms and a deep neural network (DNN), each paired with five molecular fingerprint representations, along with five graph neural network architectures (GCN, GAT, MPNN, AttentiveFP, and FPGNN). The SVM::RDKitDes model performed best (AUC = 0.89, F1 = 0.78, BA = 0.80), with robustness confirmed by Y-scrambling and randomized-split analyses, and SHAP analysis identified 20 key descriptors of TDP1 inhibition. The model was deployed as a web application (http://drugpred.top:5050) and standalone desktop applications (.exe) are available at https://github.com/zenghuang8006/TDP1-inhibitor-prediction. The validated model was applied to screen 201 231 compounds, followed by drug-likeness filtering and hierarchical docking, yielding 16 candidates. Biological evaluation identified compound AO65 as a potent TDP1 inhibitor (IC50 = 0.80 ± 0.02 µM), and quantum chemical calculations and docking elucidated its electronic properties and binding within the catalytic domain. This work demonstrates the value of integrating ML-driven prediction with structure-based approaches and identifies AO65 as a promising lead for further TDP1-focused investigation.

Decoding MLK4 Inhibition with Interpretable Machine Learning: Identification of a Novel Dihydroimidazopyridine-Based Promising Lead Compound

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.

Targeting WEE1 kinase: an integrated machine learning–cheminformatics framework for ultra-large-scale virtual screening and novel inhibitor discovery

Identification of novel PKR inhibitory chemotypes via integrated molecular modelling and machine learning

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.

Machine Learning-Driven Drug Repositioning Identifies Putative IRAK4 Inhibitors Through Structure-Based Computational Evaluation

Interleukin-1 receptor-associated kinase 4 (IRAK4) is one of the IRAK family proteins and plays an important role in the regulation of innate and inflammatory responses. In particular, IRAK4 acts as a key regulator of the Toll-like receptor (TLR) and interleukin-1 receptor (IL-1R) signaling pathways and has attracted attention as a therapeutic target for immune and inflammatory diseases. In this study, an integrated computational approach combining machine learning, molecular docking, and molecular dynamics simulations was applied to identify putative IRAK4 inhibitor candidates. Bioactivity data of IRAK4 were obtained from the ChEMBL and PubChem databases and evaluated for multiple binary classification models. The optimized XGBoost model based on ECFP4 and PubChem fingerprints achieved an ROC-AUC of 0.996 and an average precision (AP) of 0.991 on the independent test set. After that, 20 candidate compounds with high predictive probability score were finally selected through subsequent screening of the DrugBank database. Among them, DB12168 (MK-0557), DB15040 (TP-271), and DB18152 (Zilurgisertib) exhibited favorable binding free energies and stable complex formation with IRAK4 through molecular dynamics simulations and MM-PBSA calculations. Overall, these results demonstrate that approaches incorporating machine learning and structure-based computational analysis can be useful for discovering and prioritizing potential IRAK4 inhibitor candidates.

A synergistic deep learning and machine learning framework for screening heterocyclic compounds against ALDH1A1.

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.