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The developed XAI framework, together with the pathogen-specific predictive models, the pathogen-selective fragment lists, and their mapping onto the chemical structures, is able to provide a highly valuable and practical guide to the rational design of new antimicrobial agents.
Machine learning models for protein properties are usually reported by a single accuracy figure, which says how a model behaves on average but not whether to act on any one prediction, especially for a protein unlike anything in the training set. That gap is both a black box problem and an out-of-distribution problem,...
RegTrust-XAI is developed and evaluated, a trust-aware framework separating these questions using three inference-time signals: ensemble consensus, motif-grounded attribution coherence, and applicability-domain distance, which gives a more complete basis for deciding when to act on a prediction than accuracy alone.
Predicting the electronic properties of transition metal complexes (TMCs) from 2D molecular graphs remains challenging; organic-trained property models lack TMC transferability, universal interatomic potentials require 3D coordinates rather than SMILES, and tools providing holistic electronic property prediction with a...
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