Deep learning-assisted virtual screening of a large chemical library for selective GSK3β inhibitors.

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.

Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors.

Explainable Machine Learning-Guided Virtual Screening and Molecular Docking for Identification of Novel FYN Kinase Inhibitors

FYN kinase is a non-receptor protein tyrosine kinase involved in various cancers and neurodegenerative diseases; however, no selective FYN inhibitor has been approved yet. Here we introduce the explainable Machine Learning (ML) coupled with virtual screening and Molecular Docking (MD) pipeline for fast prediction of new FYN kinase inhibitors. In this study, we constructed the training set of 906 molecules active against FYN kinase from the ChEMBL database. Molecules were encoded with Extended-Connectivity Fingerprints (ECFP4). The classification models Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were developed, and the latter showed the better performance in test (AUC=0.8118) and 5-fold cross-validation (AUC=0.8297). Based on the SHapley Additive exPlanations (SHAP) values obtained via TreeExplainer, nitrogen-containing heterocycles and hydrogen bond acceptors have been identified as the most important molecular substructures. Using the optimal XGBoost classifier, screening of 2,000 approved drugs has been performed, resulting in 470 hit molecules (23.5% hit rate). Five best molecules were further submitted to the MD procedure using AutoDock Vina to dock to FYN kinase domain (PDB RCSB: 2DQ7), showing binding energies in the interval of -9.57 to -6.32 kcal/mol. Dasatinib Anhydrous (CHEMBL1421) was the second strongest binder (-8.49 kcal/mol), effectively interacting with the ATP binding site. Although CHEMBL1171837 was the strongest binder (-9.57 kcal/mol), it was caught in the ADMET profiling. According to ADMET profiling, the top one inhibitor (CHEMBL1421) satisfies Lipinski’s rule of five and Veber rules. Analysis of hydrogen bond and hydrophobic interactions revealed hydrogen bonding with ASP148, LYS39, and ASN86 and hydrophobic interactions with ALA147, ILE80, and GLY88. Validation by self-docking procedure (self-docking or STS) showed low Root Mean Square Deviation (RMSD)<2.0 Å with a binding affinity of -11.53 kcal/mol. This work highlights how explainable ML can be used in combination with structure-based docking to expedite the drug discovery process against FYN kinase and can be applied to other kinase targets.

An integrated deep learning and molecular dynamics pipeline for the discovery of novel CDK8 inhibitors against acute myeloid leukaemia.

An integrated computational pipeline combining neural network-based potency prediction with molecular dynamics simulations for CDK8 inhibitor discovery was developed and novel molecular structures beyond the training distribution were generated.

Deep learning-driven discovery and optimization of natural LSD1 inhibitors for the treatment of Alzheimer's disease.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder with limited effective disease-modifying treatments. Lysine-specific demethylase 1 (LSD1) has emerged as a promising target for AD therapy. However, current LSD1 inhibitors for AD still suffer from poor brain permeability, off-target toxicity, and chemical-scaffold scarcity. Herein, we developed a multimodal deep learning model (PLM-CAFT-DTA) for drug-target affinity (DTA) prediction. This model integrates ChemBERTa, ESM-2, graph attention, and cross-attention fusion to achieve high prediction precision. Using this model combined with virtual screening and molecular simulation, we identified silybin as a hit compound from a library of over 70,000 natural products. After rational modification, compound S3 was obtained with significantly improved LSD1 inhibition (IC₅₀ = 2.30 μM), approximately 7-fold more potent than the silybin. In vitro assays showed that S3 exhibited favorable neuroprotective and antioxidant activities. In APP/PS1 mice, S3 upregulated hippocampal H3K9me2, suppressed neuroinflammation and Aβ deposition, and improved cognitive function. By addressing unmet demands for AI-assisted anti-AD lead discovery, this study provides a generalized DTA tool for early-stage drug development, and identifies S3 as a novel LSD1 inhibitor with potent anti-AD efficacy.

From Descriptor Learning to Binding Stability: An Explainable Machine Learning Pipeline for EGFR Double-Mutant Inhibitor Discovery

An integrated computational workflow combining explainable machine learning, virtual screening, molecular dynamics simulations, and binding free-energy calculations to identify novel inhibitors of this drug-resistant EGFR variant may support the development of new therapeutic strategies for overcoming resistance in EGFR-driven cancers.