The rise and extent of antimicrobial resistance demand computational tools that go beyond simple predictions of antimicrobial activity to deliver applicable medicinal chemistry insights for an accelerated and more efficient development of novel antimicrobials. Here, we present a fragment-based explainable artificial...
Abdulmujeeb T. Onawole, M. Blaskovich, Johannes Zuegg· ACS Infectious Diseases· 0 citations
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,...
Abdulmujeeb T. Onawole, Rukayat O. Adegoke· bioRxiv· 1 citation
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.
Abdulmujeeb T. Onawole, Sulaimon Basiru, M. Sanni et al.· bioRxiv· 0 citations
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...
Abdulmujeeb T. Onawole· Journal of Chemical Informat...· 2 citations
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