Carlos Jesús García Hernández
PhD Programme: Nanoscience, Materials and Chemical Engineering
Research group: BIOCENIT - Bioinformatics & Computational Environmental Engineering
Supervisors: Alberto Fernández Sabater & Francesc Serratosa Casanelles
Bio
Carlos García-Hernández has studied Electronic Engineering degree in UNET University in Venezuela. As a final degree project, he worked on a self-driving robot moving through a labyrinth, learning all the different paths and finding the shortest way out. He also worked as a teacher assistant in programming at UNET for several semesters. After the engineering degree, he worked in different private companies in the field of industrial automation, database analysis, robotics, and mobile development. This work period helped him to improve his programming skills and originated a particular interest in computer science and machine learning. Moreover, he studied a Master degree in Computer Security Engineering and Artificial Intelligence at the URV. His master thesis was about "Active and Interactive Graph Matching in Computer Vision". This work helped him to continuous his research as a PhD student in the same university in the PhD programme of Nanoscience, Materials and Chemical Engineering. As a researcher, he is trying to follow his passion for science by participating in different conferences, workshops, seminars and summer schools in Europe. Additionally he works as a teacher assistant in mathematics and statistics in the same university.
Project: Structural pattern recognition for chemical-compound virtual screening
Molecules are naturally shaped as networks, making them ideal for studying by employing their graph representations, where nodes represent atoms and edges represent the chemical bonds. An alternative for this straightforward representation is the extended reduced graph, which summarizes the chemical structures using pharmacophore-type node descriptions to encode the relevant molecular properties. Once we have a suitable way to represent molecules as graphs, we need to choose the right tool to compare and analyze them. Graph edit distance is used to solve the error-tolerant graph matching; this methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications (known as edit operations) have an edit cost (also known as transformation cost) associated, which must be determined depending on the problem. This study investigates the effectiveness of a graph-only driven molecular comparison employing extended reduced graphs and graph edit distance as a tool for ligand-based virtual screening applications. Those applications estimate the bioactivity of a chemical employing the bioactivity of similar compounds. An essential part of this study focuses on using machine learning and natural language processing techniques to optimize the transformation costs used in the molecular comparisons with the graph edit distance. Overall, this work shows a framework that combines graph reduction and comparison with optimization tools and natural language processing to identify bioactivity similarities in a structurally diverse group of molecules. We confirm the efficiency of this framework with several chemoinformatic tests applied to regression and classification problems over different publicly available datasets.
Open Access publications
- Carlos Garcia-Hernandez, Alberto Fernández, and Francesc Serratosa. Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure, J. Chem. Inf. Model. 2019, 59, 4, 1410-1421. View full-text
- Garcia-Hernandez C, Fernández A, Serratosa F. Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening. Curr Top Med Chem. 2020;20(18):1582-1592. View full-text
- PHD THESIS: Structural pattern recognition for chemical-compound virtual screening
Outreach activities
- European Researchers’ Night 2019: “Medicamentos por ordenador”.
International secondment
- Normandy University, France. 3 months (2020).
Awards & Prizes
- 1st prize of the “Best Poster Awards” in the 16th Doctoral Day of the Doctoral Programme in Nanoscience, Materials and Chemical Engineering.
