Combined machine learning models, docking analysis, molecular dynamics and experimental validation for the rapid design of novel FLT3 inhibitors against AML DOI Creative Commons
Yihuan Zhao,

Qiang Huang,

Qiang Liu

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 10, 2024

Abstract Acute myeloid leukemia (AML) is a malignant clonal disorder driven by the excessive proliferation of immature cells in bone marrow and blood, often linked to Fms-like tyrosine kinase 3 (FLT3) mutations, which occur about one-third AML patients. While FLT3 inhibitors such as Midostaurin, Quizartinib, Gilteritinib have demonstrated clinical efficacy, their therapeutic potential limited drug resistance adverse reactions. Therefore, development novel critical for improving treatment outcomes. In this study, we employed multi-faceted computer-aided design (CADD) approach, integrating machine learning (ML), molecular docking, dynamics simulations, accelerate discovery new inhibitors. A learning-based classification model achieved an accuracy 0.958, while MV4-11 cell activity prediction strong predictive performance with R2 0.846, MAE 0.368, RMSE 0.492. Virtual screening 7,280 compounds from ChemDiv database led identification 68 inhibitors, simulations confirming stable binding protein. Experimental validation four selected showed promising cellular assays, demonstrating reliability integrated CADD approach. These results underscore CADD-driven enhanced ML, rapidly treatment.

Язык: Английский

Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis DOI

Aga Basit Iqbal,

Tariq Masoodi, Ajaz A. Bhat

и другие.

Molecular Diversity, Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

Язык: Английский

Процитировано

1
Development and validation of a new diagnostic prediction model for NAFLD based on machine learning algorithms in NHANES 2017-2020.3 DOI
Yazhi Wang,

Peng Wang

HORMONES, Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Язык: Английский

Процитировано

0
Combined machine learning models, docking analysis, molecular dynamics and experimental validation for the rapid design of novel FLT3 inhibitors against AML DOI Creative Commons
Yihuan Zhao,

Qiang Huang,

Qiang Liu

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 10, 2024

Abstract Acute myeloid leukemia (AML) is a malignant clonal disorder driven by the excessive proliferation of immature cells in bone marrow and blood, often linked to Fms-like tyrosine kinase 3 (FLT3) mutations, which occur about one-third AML patients. While FLT3 inhibitors such as Midostaurin, Quizartinib, Gilteritinib have demonstrated clinical efficacy, their therapeutic potential limited drug resistance adverse reactions. Therefore, development novel critical for improving treatment outcomes. In this study, we employed multi-faceted computer-aided design (CADD) approach, integrating machine learning (ML), molecular docking, dynamics simulations, accelerate discovery new inhibitors. A learning-based classification model achieved an accuracy 0.958, while MV4-11 cell activity prediction strong predictive performance with R2 0.846, MAE 0.368, RMSE 0.492. Virtual screening 7,280 compounds from ChemDiv database led identification 68 inhibitors, simulations confirming stable binding protein. Experimental validation four selected showed promising cellular assays, demonstrating reliability integrated CADD approach. These results underscore CADD-driven enhanced ML, rapidly treatment.

Язык: Английский

Процитировано

0