Nature methods · 2019
Kevin K Yang、Zachary Wu、Frances H Arnold
Protein engineering through machine-learning-guided directed evolution enables the optimization of protein functions. Machine-learning approaches predict how sequence maps to function in a data-driven manner without requiring a detailed model of the underlying physics or biological pathways. Such methods accelerate directed evolution by learning from the properties of characterized variants and using that information to select sequences that are likely to exhibit improved properties. Here we introduce the steps required to build machine-learning sequence-function models and to use those models to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to the use of machine learning for protein engineering, as well as the current literature and applications of this engineering paradigm. We illustrate the process with two case studies. Finally, we look to future opportunities for machine learning to enable the discovery of unknown protein functions and uncover the relationship between protein sequence and function.
bioRxiv · 2025
Cyrine Chanchabi、Conception Paul、Vincent Coulon 等
暂无摘要
Nature medicine · 2024
Adam J Widman、Minita Shah、Amanda Frydendahl 等
In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition.
Journal of Medicinal Chemistry · 2026
Leonardo Bernal、Luca Pinzi、T. Martinelli 等
Despite recent therapeutic advances, treatment options for advanced, therapy-resistant, and metastatic prostate cancer (PCa) remain limited. Here, we developed and prospectively validated an integrated chemoinformatics and machine learning (ML) workflow with ligand-based similarity filtering. Validation on independent external data sets showed that this applicability-domain-guided integration strategy can reduce false positives and improve virtual screening performance. Screening of DrugBank identified five repurposing candidates with confirmed antiproliferative activity in both 2D and 3D PCa models. Among them, the antifungal agent chlormidazole emerged as the most promising candidate, displaying tumor-selective and predominantly cytostatic activity associated with p57 upregulation, reduced Rb phosphorylation, and G1 arrest. Chlormidazole also enhanced the antiproliferative activity of docetaxel in both models, achieving comparable efficacy at substantially lower docetaxel concentrations. These findings identify chlormidazole as a promising repurposing candidate for PCa and demonstrate the value of integrating chemoinformatics with ML for drug repurposing and virtual screening.
Expert opinion on drug discovery · 2022
Maria Kontoyianni
暂无摘要
Frontiers in chemistry · 2021
Luciana Scotti、Eugene Muratov、Alejandro Speck-Planche 等
暂无摘要
Scientific Reports · 2025
Bandral Sunil Kumar、Basavana Gowda Hosur Dinesh、Srinivas Ganjipete 等
Alzheimer’s disease (AD) remains one of the most challenging neurodegenerative disorders, with limited therapeutic options and high failure rates in clinical trials. This work developed a drug repurposing pipeline powered by a machine learning (ML) model to find possible glycogen synthase kinase-3 beta (GSK-3β) inhibitors, a crucial target in AD pathogenesis. We selected, pre-processed, and optimized a dataset of 4,087 experimentally verified GSK-3β inhibitors using dimensionality reduction and descriptor creation. The most excellent prediction performance was obtained by Random Forest (100 descriptors) out of six supervised ML algorithms that were studied (R2 = 0.8178, RMSE = 0.8118, MAE = 0.6084). Following the virtual screening of 1,616 Food and Drug Administration (FDA)-approved drugs using this refined model, many compounds with projected IC₅₀ < 500 nM were found. Docking experiments showed insightful interactions and high binding affinities with the active-site residues of GSK-3β. With the best docking score (–9.3 kcal/mol), stable molecular dynamics (Average RMSD values (1000 ns): protein, 2.23 ± 0.93 Å; protein–ligand complex, 1.40 ± 0.43 Å) and long-lasting contacts with crucial residues, dolutegravir stood out among the top choices. ADMET profiling validated good pharmacokinetics and safety characteristics; however, possible hepatotoxicity needs more research. A HOMO–LUMO gap of 3.07 eV was found by density functional theory (DFT) analysis, indicating robust electron transport characteristics and balanced reactivity that are favorable for protein–ligand interaction. Together, these findings show that dolutegravir is a potential repurposable option against AD and how integrative ML, docking, MD, ADMET, and quantum chemistry techniques may speed up the identification of new drugs.
Intelligent Systems Research and Applications Journal · 2026
Ahmet Turan Demir
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.
NPJ digital medicine · 2026
Jie Li、Mingqi Chen、Pan Ren 等
Alzheimer's disease (AD) is a progressive neurodegenerative disorder increasingly associated with peripheral inflammatory conditions such as chronic periodontitis (CP); however, the molecular mechanisms linking these conditions remain poorly understood. Here, we investigated the therapeutic effects of Huanglian Jieddu Decoction (HLJDD) on CP-induced AD using an integrative machine learning-guided multi-omics approach. Analysis of public single-cell RNA-sequencing data revealed pronounced inflammatory activation in microglia from AD samples. We further established a CP-induced AD rat model and performed hippocampal transcriptomic profiling. Multiple complementary machine learning strategies, including Random Forest-based feature selection, support vector machine-based refinement, network modeling, and interpretable model analysis, were applied to prioritize disease-relevant pathways from high-dimensional transcriptomic data. Across models, components of the cGAS-STING signaling pathway consistently exhibited strong and directional contributions to CP-AD pathology, indicating a central inflammatory axis linking peripheral infection to neurodegeneration. Guided by these data-driven insights, in vivo and in vitro experiments demonstrated that HLJDD suppressed cGAS-STING activation, attenuated neuroinflammation, and improved cognitive function in CP-induced AD models. Collectively, this study highlights the value of machine learning-assisted transcriptomic interpretation for mechanistic prioritization and identifies HLJDD as a multitarget therapeutic strategy for CP-induced AD.
Future Medicinal Chemistry · 2024
Miao-Miao Kong、Tao Wei、Bo Liu 等
Aim: Build a virtual screening model for ULK1 inhibitors based on artificial intelligence. Materials & methods: Build machine learning and deep learning classification models and combine molecular docking and biological evaluation to screen ULK1 inhibitors from 13 million compounds. And molecular dynamics was used to explore the binding mechanism of active compounds. Results & conclusion: Possibly due to less available training data, machine learning models significantly outperform deep learning models. Among them, the Naive Bayes model has the best performance. Through virtual screening, we obtained three inhibitors with IC50 of μM level and they all bind well to ULK1. This study provides an efficient virtual screening model and three promising compounds for the study of ULK1 inhibitors.
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