Enhancing ERα-targeted compound efficacy in breast cancer threapy with ExplainableAI and GeneticAlgorithm
Zeonlung Pun, Qiaoyun Xue, Yichi Zhang, Manikkam Rajalakshmi, Manikkam Rajalakshmi, Manikkam Rajalakshmi
TL;DR
This paper introduces a new method combining AI and genetic algorithms to improve cancer drug effectiveness by optimizing compound properties.
Contribution
A novel integration of explainable AI and genetic algorithms to enhance ERα-targeted compound efficacy and ADMET properties.
Findings
Selected 50 critical molecular descriptors from 729, improving bioactivity prediction robustness.
Optimized compounds achieved pIC50 values up to 10.05, surpassing previous peaks in the dataset.
Abstract
Breast cancer remains a major cause of mortality among women globally, driving the need for advanced therapeutic solutions. This study presents a novel, comprehensive methodology integrating explainable artificial intelligence (AI), machine learning models, and genetic algorithms to enhance the bioactivity and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties of compounds targeting estrogen receptor alpha (ERα). By employing SHAP (SHapley Additive exPlanations) and LassoNet, we identified and refined 50 critical molecular descriptors from an initial set of 729, significantly influencing the prediction of bioactivity. The selected descriptors were systematically validated, bolstering the predictive robustness of our models, which demonstrated a mean coefficient of determination of 77% for bioactivity and high accuracy scores of 90.2%, 93.7%, 89.5%, 87.3%,…
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Taxonomy
TopicsComputational Drug Discovery Methods · Bioinformatics and Genomic Networks · Estrogen and related hormone effects