Swarit Jasial
Assistant Professor
swarit.jasial@mahindrauniversity.edu.in
Having a background in Bioinformatics, Swarit Jasial did his Masters in Life Science Informatics and PhD in Computational Life Sciences from University of Bonn, Germany. During his studies, Swarit was involved in several projects concerning data mining and machine learning. He worked in the research area of computer aided drug design or Chemoinformatics. His PhD work focused on analysis of multitarget activities and assay interference characteristics of pharmaceutically relevant compounds. He studied promiscuity of compounds present in publicly available databases and his data analysis revealed interesting results for state-of-the-art PAINS filters, which are generally used to flag assay interference compounds. The machine learning models built in this project further extended the capacity of PAINS filters as they also took structural context into account. He published his studies in several journals mostly belonging to American Chemical Society (ACS).
Swarit Jasial did his post doctoral studies (2019-2024) in Data-Driven Chemistry lab, Data Science Center, Nara Institute of Science and Technology, Japan where he also worked as a specially appointed assistant professor. He worked on projects such as predicting antiviral activity of odorants with Kao corporation and monomer concentration prediction of polymerization reactions from infra-red spectra in collaboration with JSR corporation. He also supervised students in their research focusing on Chemoinformatics.
Ph.D.
Ph.D. in Computational Life Sciences, Bonn-Aachen International Center for Information Technology (B-IT), Rheinische Friedrich-Wilhelms-Universität Bonn, Germany (2015-2019)
M.Sc.
M.Sc. in Life Science Informatics, Bonn-Aachen International Center for Information Technology (B-IT), Rheinische Friedrich-Wilhelms-Universität Bonn, Germany (2012-2014)
B.Tech.
B.Tech. in Bioinformatics, Jaypee University of Information Technology, Waknaghat, India (2007-2011)
2019-2024
Specially Appointed Assistant Professor, Data-Driven Chemistry Laboratory,Data Science Center, Division of Materials Science, Nara Institute of Science and Technology (NAIST), Nara, Japan
Publications
2023
- Wakiuchi, A.; Jasial, S.; Asano, S.; Hashizume, R.; Hatanaka, M.; Ohnishi, Y.; Matsubara, T.; Ajiro, H.; Sugawara, T.; Fujii, M.; Miyao, T. Chemometrics Approach Based on Wavelet Transforms for the Estimation of Monomer Concentrations from FTIR Spectra. ACS Omega 2023, 8, 19781-19788.
2022
- Jasial, S.; Hu, J.; Miyao, T.; Hirama, Y.; Onishi, S.; Matsui, R.; Osaki, K.; Funatsu, K. Screening and Validation of Odorants against Influenza A Virus Using Interpretable Regression Models. ACS Pharmacol. Transl. Sci. 2022, 6, 139-150.
2021
- Tamura, S.; Jasial, S.; Miyao, T.; Funatsu, K. Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel. Molecules 2021, 39, 2000103.
2019
- Miyao, T.; Jasial, S.; Bajorath, J.; Funatsu, K. Evaluation of Different Virtual Screening Strategies on the Basis of Compound Sets with Characteristic Core Distributions and Dissimilarity Relationships. J. Comput. Aided Mol. Des. 2019, 33, 729-743.
2018
- Jasial, S.; Gilberg, E.; Blaschke, T.; Bajorath, J. Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter. J. Med. Chem. 2018, 61, 10255-10264.
- Vogt, M.; Jasial, S.; Bajorath, J. Extracting Compound Profiling Matrices from Screening Data. ACS Omega 2018, 3, 4706-4712.
2017
- Jasial, S.; Bajorath, J. Dark Chemical Matter in Public Screening Assays and Derivation of Target Hypotheses. Med. Chem. Commun. 2017, 8, 2100–2104.
- Jasial, S.; Hu, Y.; Bajorath, J. How Frequently Are Pan-Assay Interference Compounds Active? Large-Scale Analysis of Screening Data Reveals Diverse Activity Profiles, Low Global Hit Frequency, and Many Consistently Inactive Compounds. J. Med. Chem. 2017, 60, 3879-3886.
2016
- Jasial, S.; Hu, Y.; Vogt, M.; Bajorath, J. Activity-Relevant Similarity Values for Fingerprints and Implications for Similarity Searching. F1000Research 2016, 5 (Chem. Inf. Sci.): 591.
- Jasial, S.; Hu, Y.; Bajorath, J. Determining the Degree of Promiscuity of Extensively Assayed Compounds. PLoS One 2016, 11, e0153873.
- Jasial, S.; Hu, Y.; Bajorath, J. Assessing the Growth of Bioactive Compounds and Scaffolds over Time: Implications for Lead Discovery and Scaffold Hopping. J. Chem. Inf. Model. 2016, 56, 300-307.
2015
The main theme of our research is Computer-Aided Drug Design or Chemoinformatics. It is the application of informatics in the field of Chemistry where we utilize data related to compounds or approved drugs against certain diseases. We try to develop new computational techniques or models to analyze and predict activity of compounds against different targets in order to find potential drug candidates. These drug candidates can further be proposed for validations in drug discovery programs.
Our research focuses on finding trends in data (data mining) and machine learning with biological and chemical data. With machine learning algorithms, computers are able to learn structural patterns or information which might be responsible for the activity of a certain compound against a biological target. We are passionate to know how does an algorithm work in finding hidden patterns in the data and how to make a machine learn related to specific problem in hand. Therefore, we work on the developmental aspect of models/techniques and their interpretability.
Our current research interests include integration of knowledge from various domains for better predictions and rationalization, utilization of generative AI for de novo molecular design, targets and pathway identification for natural products.
