ALMERIA: Boosting Pairwise Molecular Contrasts with Scalable Methods
Volume 35, Issue 3 (2024), pp. 617–648
Pub. online: 14 May 2024
Type: Research Article
Open Access
Open Access
Received
1 June 2023
1 June 2023
Accepted
1 April 2024
1 April 2024
Published
14 May 2024
14 May 2024
Abstract
This work introduces ALMERIA, a decision-support tool for drug discovery. It estimates compound similarities and predicts activity, considering conformation variability. The methodology spans from data preparation to model selection and optimization. Implemented using scalable software, it handles large data volumes swiftly. Experiments were conducted on a distributed computer cluster using the DUD-E database. Models were evaluated on different data partitions to assess generalization ability with new compounds. The tool demonstrates excellent performance in molecular activity prediction (ROC AUC: 0.99, 0.96, 0.87), indicating good generalization properties of the chosen data representation and modelling. Molecular conformation sensitivity is also evaluated.
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Biographies
Mena-Yedra Rafael
R. Mena-Yedra is a senior data scientist. He holds a PhD in computing from the Universitat Politècnica de Catalunya (UPC) in Barcelona, Spain. His expertise lies in industrial data science, where he has applied AI/ML techniques across various domains including energy demand modelling, automatic control of microalgae photobioreactors, transportation and mobility research, and has made contributions to the financial industry and cheminformatics. With his diverse experience, he currently works in the AI industry creating innovative solutions to complex problems.
López Redondo Juana
J. López Redondo is a distinguished full professor in the Department of Computer Science at the University of Almería, Spain. With a PhD in advanced computer techniques, she focuses her research on high-performance computing, optimization applications, and the design of solar energy systems. Her significant contributions to these fields have earned her recognition at international conferences and publications in various international journals. She is also an integral part of the research group TIC 146 Supercomputación-Algoritmos (SAL), established in 1995.
Pérez-Sánchez Horacio
H. Pérez-Sánchez is a distinguished researcher currently serving a principal investigator of the Structural Bioinformatics and High Performance Computing research group at the Catholic University of Murcia (UCAM) in Murcia, Spain. He earned his PhD in computational chemistry from the University of Murcia and now holds a full professorship at UCAM, where he teaches various disciplines including biotechnology, pharmacy, medicine, and computer engineering.
Martinez Ortigosa Pilar
P. Martınez Ortigosa is a distinguished full professor at the University of Almeria, Spain, where she also earned her PhD in computer science. She stands out in the Department of Computer Science at the same institution. Her research is primarily centred on creating high performance computing software and tools. These tools are designed to tackle a broad spectrum of issues and applications originating from various scientific and technological fields. She contributes her expertise to the research group TIC 146 Supercomputación-Algoritmos (SAL), a group that has been active since 1995.
