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Proceeding Paper

LIFE.PTML Model Development Targeting Calmodulin Pathway Proteins †

by
Maider Baltasar-Marchueta
1,
Naia López
1,
Sonia Arrasate
1,
Matthew M. Montemore
2 and
Humberto González-Díaz
1,3,4,*
1
Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, 48940 Leioa, Bizkaia, Spain
2
Department of Chemical and Biomolecular Engineering, Tulane University, New Orleans, LA 70118, USA
3
Biofisika Institute, Spanish National Research Council-University of the Basque Country (CSIC-UPV/EHU), 48940 Leioa, Bizkaia, Spain
4
IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Bizkaia, Spain
*
Author to whom correspondence should be addressed.
†
Presented at the 29th International Electronic Conference on Synthetic Organic Chemistry, 14–28 November 2025; Available online: https://sciforum.net/event/ecsoc-29.
Chem. Proc. 2025, 18(1), 38; https://doi.org/10.3390/ecsoc-29-26890
Published: 3 December 2025
(This article belongs to the Proceedings of The 29th International Electronic Conference on Synthetic Organic Chemistry)
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Abstract

Developing predictive models for drug efficacy is challenged by the complexity and heterogeneity of bioassay data. Here, we present LIFE.PTML, which is a methodology integrating drug Lifecycle (L), Information Fusion (IF), Encoding (E), Perturbation Theory (PT), and Machine Learning (ML) to predict compound activity across diverse experimental conditions. Using a dataset of 3748 molecule–assay combinations targeting calmodulin (CaM) and related proteins, LIFE.PTML combines chemical and protein descriptors, quantifies experimental variability via perturbation operators, and trains non-linear classifiers, including XGBoost and Gradient Boosting. XGBoost achieved the best performance, with 88.9% test accuracy and an ROC AUC of 0.959, while feature importance analysis highlighted contributions from both drug- and protein-level descriptors. The results demonstrate that LIFE.PTML provides a robust, flexible, and interpretable framework for predictive chemoinformatics, facilitating the integration of multi-source data for drug discovery applications.
Keywords: drug discovery; calmodulin; chemoinformatics; machine learning; LIFE.PTML drug discovery; calmodulin; chemoinformatics; machine learning; LIFE.PTML

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MDPI and ACS Style

Baltasar-Marchueta, M.; López, N.; Arrasate, S.; Montemore, M.M.; González-Díaz, H. LIFE.PTML Model Development Targeting Calmodulin Pathway Proteins. Chem. Proc. 2025, 18, 38. https://doi.org/10.3390/ecsoc-29-26890

AMA Style

Baltasar-Marchueta M, López N, Arrasate S, Montemore MM, González-Díaz H. LIFE.PTML Model Development Targeting Calmodulin Pathway Proteins. Chemistry Proceedings. 2025; 18(1):38. https://doi.org/10.3390/ecsoc-29-26890

Chicago/Turabian Style

Baltasar-Marchueta, Maider, Naia López, Sonia Arrasate, Matthew M. Montemore, and Humberto González-Díaz. 2025. "LIFE.PTML Model Development Targeting Calmodulin Pathway Proteins" Chemistry Proceedings 18, no. 1: 38. https://doi.org/10.3390/ecsoc-29-26890

APA Style

Baltasar-Marchueta, M., López, N., Arrasate, S., Montemore, M. M., & González-Díaz, H. (2025). LIFE.PTML Model Development Targeting Calmodulin Pathway Proteins. Chemistry Proceedings, 18(1), 38. https://doi.org/10.3390/ecsoc-29-26890

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