Author: gracebaysinger

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.

Dr. Juan V. Alegra Requena was awarded funds for travel to present his work at the RSEQ XXXVIII Biennial meeting in Granada, Spain in June, 2022 and the 8^th EuChemS Chemistry Congress in Lisbon, Portugal in September 2022. As of April 2022, he has a Juan de la Cierva contract at the University of Zaragoza, Spain where he is establishing his own research group focusing on computational mechanisms and Machine Learning. He is additionally developing cheminformatics tools for multiple purposes such as data manipulation, automated supporting information creation and Machine Learning modeling. One of the most important codes is GoodVibes (https://github.com/bobbypaton/GoodVibes, a useful tool used to gather, analyze, check, and present thermochemistry data from multiple quantum mechanical programs.

Dr. Beatrice Chiew was awarded funds to present her work at the Royal Australian Chemistry Institute National Congress in July 2022 and the EFMC International symposium on medicinal chemistry in September. She is a a postdoc at the University of Newcastle, Australia, where she is   working on drug development in the space of Dynamin endocytosis inhibitors. Her PhD work was on the development of a novel fragment-to-lead workflow to expedite the development of chemical probes against the DNA damage protein 53BP1 for use against BRCA-1 breast cancer. She continues to explore these fragments through purchasable analogues using a novel cheminformatic algorithm called GRADe

Daniel Csókás, a member of Professor Imre Pápai’s research team at the Research Centre for Natural Sciences, Budapest, Hungary was awarded funds to travel to the University of Bristol (UK) to expand the scope of his experience in computational chemistry and to acquire new skills and research techniques in the area of data-led catalyst design. The project will involve the creation of a ligand knowledge base for tridentate ligands using calculated descriptors. The database will then be processed to retrieve structural and reactivity information about tridentate ligands and their transition metal complexes. The award is pending the lifting of travel restrictions due to the pandemic.

Andrew Tarzia, a Research Associate at the Imperial College London, was awarded funds to visit Asst. Prof. Cory Simon at Oregon State University for three weeks in 2021 to initiate a collaboration in the use of machine learning algorithms to predict host-guest finding affinities based upon molecular shapes. The award is pending the lifting of travel restrictions due to the pandemic.  

Nicola Knight, an Enterprise Research Fellow – Physical Sciences Data-Science Service (PSDS) at the University of Southampton (UK) where she works with the newly-established national research facility to provide access to chemistry and physical sciences data at a national scale and to increase not only the breadth of the data, but also the ways in which the data can be used by the scientific community. She was awarded funds to support efforts related to a knowledge sharing retreat that will involve four early-career researchers (ECRs) from a cross section of research domains to participate in a 3-day workshop on the depiction of chemical information using the FAIR principles.

2021 Grant Awardees

Call for Grant proposals not circulated due to the pandemic.