arXiv subjects

Michael Nicholls

Publications and source records attributed to Michael Nicholls.

2 recordsLinked to original sources

arXiv2025

Machine Learning - driven insights for predicting the impact of nanoparticles on the functionality of biomolecules, Illustrated by the case of DNA Damage-Inducible Transcript 3 (CHOP) inhibitors

This study introduces a pioneering machine learning (ML)-based approach for predicting the impact of nanoparticle (NP) carriers on the functionality of attached small biomolecules. It was hypothesised that NP interactions induce measurable perturbations in the atomic environment of the small biomolecules, which are reliably captured by chemical shifts in 13C and 1H NMR spectroscopy. Ten datasets were generated by combining 13C, 1H NMR spectroscopy data, derived from SMILES notations and molecular features provided by PubChem. The resulting datasets were used to train predictive models via traditional ML algorithms (Scikit-learn) and Deep Neural Network DNN (PyTorch). The methodology was demonstrated through a quantitative high-throughput screening (qHTS) focused on DNA Damage-Inducible Transcript 3 (CHOP) inhibitors. The optimal ML performance was achieved by the Random Forest Classifier, which was trained on 19,184 samples and tested on 4,000, resulting in 81.1% accuracy, 83.4% precision, 77.7% recall, 80.4% F1-score, 81.1% ROC, and a five-fold cross-validation score of 0.821. Complementing the main study, two computational approaches were developed to enhance CHOP inhibitor prediction. The first identifies the most desirable/undesirable functional groups for CHOP inhibition. The second, a CID_SID ML model, achieved 90.1% accuracy in predicting whether compounds designed for other purposes possess CHOP inhibition potential.

q-bio.QM↗

arXiv2025

In Silico Functional Profiling of Engineered Small Molecules: A Machine Learning Approach Leveraging PubChem Identifiers (CID_SID ML model)

The article introduces a concept for a time- and cost-effective methodological framework leveraging machine learning (ML) models for both early-stage drug development and clinical trial support. The rationale for this approach is the inherent scalability and speed enabled by using pre-calculated data embedded in existing PubChem identifiers (CID and SID), thereby eliminating the computationally intensive step of on-the-fly molecular descriptor generation. The approach was effectively demonstrated across four diverse bioassays: antagonists of the human D3 dopamine receptor, Rab9 promoter activators, small-molecule inhibitors of CHOP, and antagonists of the human M1 muscarinic receptor. A comparison, based on Matthews correlation coefficient (MCC), was conducted between the CID_SID ML model, the MORGAN2-based ML model, and the RDKit-transformed SMILES model for these four case studies, revealing that no method is universally superior in terms of performance. Furthermore, the CID_SID model averaged a rapid execution time of only 3.3 seconds; the ML models relying on explicit structural descriptors, such as MORGAN2 and RDKit-transformed SMILES, demonstrated high computational costs, with processing times averaging 106.0 and 109.6 seconds, respectively. While negligible for a single ML model, these times would cause a significant difference in computational resource consumption when scaled across a framework involving over a million buildings. Moreover, the CID_SID ML model achieved strong average performance metrics: Accuracy of 83.52%, Precision of 89.62%, Recall of 75.65%, F1-Score of 81.93% and ROC of 83.53%.

q-bio.QM↗