PRE-PRINT: Suman Chakravarti. “Computational Prediction of Metabolic alpha-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment.” https://chemrxiv.org/engage/chemrxiv/article-details/640e8128b5d5dbe9e82a821c
Girireddy, Mounika, Roustem Saiakhov, and Suman Chakravarti. “Assessment of Endocrine Disruption Potential of Chemicals Using Combined Quantitative Structure-Activity Relationship Modeling of In Vitro and In Vivo Assays.” Applied In Vitro Toxicology 8, no. 4 (December 1, 2022): 139–46. https://doi.org/10.1089/aivt.2022.0015.
Chakravarti, S.K., (2022). Scalable quantitative structure-activity relationship systems for predictive toxicology. In: Basak, Subhash C., and Marjan Vracko, eds. Big Data Analytics in Chemoinformatics and Bioinformatics: With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology. Amsterdam: Elsevier. ISBN: 9780323857130 https://doi.org/10.1016/B978-0-323-85713-0.00031-1
Faramarzi, Sadegh, Marlene T Kim, Donna A Volpe, Kevin P Cross, Suman Chakravarti, and Lidiya Stavitskaya. Development of QSAR Models to Predict Blood-Brain Barrier Permeability. Frontiers in Pharmacology, n.d., 13. https://doi.org/10.3389/fphar.2022.1040838
Chakravarti, S.K., Saiakhov, R.D. (2022). MultiCASE Platform for In Silico Toxicology. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 2425. Humana, New York, NY.
https://doi.org/10.1007/978-1-0716-1960-5_19
Management of pharmaceutical ICH M7 (Q)SAR predictions – The impact of model updates
Hasselgren et al, Regulatory Toxicology and Pharmacology Volume 118, December 2020
https://doi.org/10.1016/j.yrtph.2020.104807
Reason Vectors: Abstract Representation of Chemistry-Biology Interaction Outcomes, for Reasoning and Prediction J. Suman Chakravarti, Chem. Inf. Model. 2020 Publication Date: September 22, 2020
https://doi.org/10.1021/acs.jcim.0c00601
Descriptor Free QSAR Modeling Using Deep Learning with Long Short-Term Memory Neural Networks, Suman K. Chakravarti and Sai Radha Mani Alla; Frontiers, August 22nd, 2019 DOI: 10.3389/frai.2019.00017 https://www.frontiersin.org/articles/10.3389/frai.2019.00017/abstract
Computing similarity between structural environments of mutagenicity alerts, Chakravarti, S.K., Saiakhov, R. D.; Mutagenesis, October 20, 2018, DOI: https://doi.org/10.1093/mutage/gey032
Distributed Representation of Chemical Fragments; Chakravarti S.K.; ACS Omega. 2018; 3(3): 2825-2836. DOI: 10.1021/acsomega.7b02045
Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches; Kim M.T., Sedykh A., Chakravarti S.K., Saiakhov R.D., Zhu H.; Pharm Res. 2014; 31(4):1002-14. DOI: 10.1007/s11095-013-1222-1
Effectiveness of CASE Ultra Expert System in Evaluating Adverse Effects of Drugs; Saiakhov, R.D., Chakravarti, S.K. and Klopman, G.; Molecular Informatics, 2012, 32, 87-97. DOI: 10.1002/minf.201200081
Optimizing predictive performance of CASE Ultra expert system models using the applicability domains of individual toxicity alerts; Chakravarti, S.K., Saiakhov, R.D. and Klopman, G., Journal of Chemical Information and Modeling, 2012, 52, 2609-2618. DOI: 10.1021/ci300111r
Big Data Based Machine Learning Approach for Improving Analog Search for Mutagenicity Prediction
An Improved Workflow to Perform In Silico Mutagenicity Assessment of Impurities As Per ICH M7 Guideline; Suman Chakravarti, Alexander Sedykh, and Roustem Saiakhov; Society of Toxicology Meeting, Phoenix Arizona, March 2014.
Developing QSAR models of major human membrane transporters for the application in drug design and drug safety evaluations; Alexander Sedykh, Suman Chakravarti, and Roustem Saiakhov; Society of Toxicology Meeting, Phoenix Arizona, March 2014.
Interpretable QSAR of skin sensitization for screening cosmetics and environmental chemicals; Roustem Saiakhov, Suman Chakravarti, and Alexander Sedykh; Society of Toxicology Meeting, Phoenix Arizona, March 2014.
A novel hybrid approach in combining a QSAR based expert system and a quantitative read across methodology to achieve better in silico genotoxicity assessment of drugs, impurities and metabolites; Suman Chakravrti, Roustem Saiakhov, Gilles Klopman; Society of Toxicology Meeting, San Antonio, TX; March 2013.
Development of Improved QSAR Models for Predicting A-T Base Pair Mutations; Lidiya Stavitskaya, Barbara L. Minnier, R. Daniel Benz, and Naomi L. Kruhlak; FDA Center for Drug Evaluation and Research (CDER); Genetic Toxicology Association Meeting, University of Delaware, October 2013.
Kruhlak, N. L.; Chakravarti, S.; Kumaran, G.; Saiakhov, R. A., New Structural Similarity Method to Identify Surrogate Compounds for Assessing the Carcinogenicity of Nitrosamine Impurities, Society of Toxicology Annual Meeting, San Diego, 2022.Characterization and Application of an External Validation Set for Salmonella Mutagenicity QSAR Models Using Structural Fingerprints of Known Toxicophores; Naomi L. Kruhlak, Kevin P. Cross, Barbara L. Minnier, David A. Bower, R. Daniel Benz; Society of Toxicology Meeting, San Antonio, TX; March 2013.