David Dalmau Ginesta, who received a Master’s degree in 2021 from the University of Zaragoza (Spain). He was awarded a highly competitive “pre-doctoral grant” from the Aragon Government to pursue his PhD studies in the same laboratory. He incorporated Dr. Juan V. Alegre Requena as a new supervisor to improve his skills in computational chemistry and programming and started developing cheminformatics and machine learning tools applicable to various sectors of computational chemistry (Note: Dr. Alegre Requena was a 2022 recipient of the CSA Trust Grant for the purpose of establishing his own research group). Ginesta is requesting funds to attend two international conferences (the RSEQ XXXIX Biennial Meeting in Zaragoza (Spain), June 25-29, 2023, and the ACS fall in San Francisco (USA), August 13-17, 2023. He will be presenting papers at both conferences on the ROBERT program that his research group has recently developed. This is a free and open-source Python package aiming to provide high-level ML protocols accessible to all users via GitHub. The software includes five modules: curate, generate, testing, predict and AQME-ROBERT, offering complete and diverse ML workflows. Input options include CSV and JSON databases with descriptors, as well as SMILES inputs via the AQME-ROBERT tandem. ROBERT is easy to install and can be used via command lines, Jupyter Notebooks, and SLURM scripts. Overall, ROBERT provides users with a comprehensive, transparent, and reproducible ML workflow to generate highly accurate predictions with minimal user intervention. This program has potential in multiple chemistry fields, including drug discovery, materials science, chemical synthesis, and catalyst discovery.
Heidi Klem will be receiving her PhD from Colorado State University (USA) in June 2023 and was selected to be a National Research Council postdoctoral associate at the U.S. National Institute of Standards and Technology (NIST) as of August 2023 as a result of her research proposal. She was awarded funds to spend a month visiting Professor Zhongyue John Yang at Vanderbilt University, Nashville, TN (USA) for one month. Prof. Yang is the SC Family Dean’s Faculty Fellow and an Assistant Professor of Chemistry, Chemical Engineering, and Data Science. He is a prominent investigator in the field of data-driven and high-throughput enzyme modeling with notable contributions such as EnzyHTP,1 and IntEnzyDB.2 EnzyHTP is a state-of-the-art Python software that automates model generation, QM, MM and multiscale (i.e., QM/MM) simulations, and data analysis. During her visit she will be immersed in Prof. Yang’s research group to gain direct training in the software development and application of EnzyHTP and IntEnzyDB. These training experiences will contribute significantly to the success of her NIST research proposal – the development of a standardized protocol for the generation and validation of quantum mechanical QM-based enzyme models. The results of this work will be incorporated into a model generation and validation workflow, available to the community as an open-source, Python-based package. As noted by the National Academies of Sciences, Engineering, and Medicine in a 2019 consensus report, Reproducibility and Replicability in Science, computational workflows are exceedingly valuable to facilitate reproducibility. This workflow can be used in the development of NIST Standard Reference Data (SRD) in the form of pre-validated enzyme models to expedite biocatalysis investigations, validate new methods on previously benchmarked systems and promote data-driven characterization of enzyme scaffold design principles
Dr. Md Bin Yeamin, received his PhD in computational and experimental chemistry in 2021 from the University Rovira I Virgili, Spain. He is currently a postdoctoral fellow at the University of Girona, researching dual catalysis in alkyne functionalization and predictive catalysis in hydrogenation and CO2 fixation. With his expertise in structured Python and ML techniques, he intends to apply Machine Learning to mechanistic modeling for predicting reaction barriers and yields. He was awarded funds to support two activities. First, a month-long research stay in June 2023 at the Alegre group at ISQCH Institute of CSIC-Universidad de Zaragona to launch a collaborative project on predicting catalytic performances and reaction mechanisms from data-driven automated machine learning (ML) methods. Second, his abstract on gem-Hydrogenation leading to Enyne Cycloisomerization” has been accepted for an oral presentation at the XXXIX Biennial Meeting – Spanish Royal Society of Chemistry (RSEQ) 2023 under the symposium S15. Recent trends in organometallic Reactivity.
Chemical Structure Association Trust 