Enhancing ERα-targeted compound efficacy in breast cancer threapy with ExplainableAI and GeneticAlgorithm
Year: 2025
Type: article
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 (ER[Formula: see text]). 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[Formula: see text] for bioactivity and high accuracy scores of 90.2[Formula: see text], 93.7[Formula: see text], 89.5[Formula: see text], 87.3[Formula: see text], and 95.8[Formula: see text] for absorption, distribution, metabolism, excretion, and toxicity, respectively. Further optimization through genetic algorithms identified candidate compounds with superior bioactivity, achieving pIC50 values as high as 10.05, surpassing the previously observed peak values in the dataset. These results underscore the potential of leveraging advanced machine learning and optimization techniques to accelerate the discovery of effective cancer therapies.
Source: PLoS ONE
Authors: Zeonlung Pun, Qiaoyun Xue, Yichi Zhang
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W4410535197