El Dr. Claudio Cavasotto He specializes in computational simulation of biomacromolecules, computer-aided drug design, and biomedical informatics. He is a Principal Investigator at CONICET and Scientific Director of the Institute of Applied Artificial Intelligence at the Universidad Austral.

He earned his BS in Physics from the University of Buenos Aires and his PhD in Physics from the same institution. He completed postdoctoral studies at The Scripps Research Institute (La Jolla, California) in the areas of computational biology and biophysics. He then joined MolSoft LLC (San Diego, CA) as a senior researcher, remaining there for five years. From 2007 to 2012, he was an associate professor at the School of Biomedical Informatics at the University of Texas Health Science Center at Houston (Houston, TX), and has since been a member of the CONICET scientific research program.

According to a recent study by Stanford University published in PLOS Biology, he is in the top 2% of the ranking of the best scientists in the world.

Developing a new drug costs up to $2.5 billion and takes up to 15 years. However, most candidate molecules fail in clinical trials due to efficacy and safety issues. This constitutes a serious obstacle, considering that patients urgently need access to effective and innovative treatments that are free of side effects and affordable. For several years now, computational (bio)chemistry has established itself as a fundamental tool in the long process of developing new drugs, enabling a more rational approach, improving efficiency, and saving time, costs, and effort.

For over 15 years, our laboratory has focused on two complementary lines of research: i) studying the relationship between the structure of biomolecular complexes, their (thermo)dynamic properties, and their function, identifying molecules that modulate therapeutic targets for a variety of diseases; and ii) developing a new generation of computational methods for computational drug design. A key component of our research is our close collaboration with leading experimental researchers from both Spain and abroad. We utilize biomolecular simulations, homology modeling of protein structures, leading drug discovery methods based on receptor and ligand structure, and artificial intelligence and cheminformatics tools.

Our research group maintains active collaborations with: Dr. Mariela Bollini (Center for Bionanoscience Research, CIBION); Dr. Philippe Diaz (University of Montana, Missoula, MT, USA); Pilar Cossio (University of Antioquia, Medellín, Colombia).

• Discovery and optimization of leading drugs for different therapeutic targets, with special emphasis on SARS-CoV-2, Dengue, Zika, and GPCRs.
• Characterization of “druggability” of viral proteomes, particularly SARS-CoV-2.
• Development of next-generation simulation methods for leading drug discovery based on molecular and quantum mechanics, and machine learning.
• Prediction of toxicity and pharmacological properties using artificial intelligence tools.

• Scardino, V., Galarce, M.J., Mignone, ME, and Cavasotto, CN (2025). Enhancing the Reliability of Integrated Consensus Strategies to Boost Docking-Based Screening Campaigns Using Publicly Available Docking Programs. Mol. Inform. 44, e2445. [doi]: http://dx.doi.org/10.1002/minf.2445
• Di Filippo, JI, Romoli, S., and Cavasotto, CN (2025). Assessing the Robustness and Scalability of Machine Learning Methods to Accelerate Ultralarge High-Throughput Docking Campaigns. ACS Omega 10, 15598-15609. [doi]: http://dx.doi.org/10.1021/acsomega.5c00829
• Cavasotto, CN, Di Filippo, J.I., and Scardino, V. (2024). Lessons learned from machine learning in early stages of drug discovery. Expert Opinion. Drug Discov. 19, 631-633. [doi]: http://dx.doi.org/10.1080/17460441.2024.2354279
• Scardino, V., Di Filippo, JI, and Cavasotto, CN (2023). How good are AlphaFold models for docking-based virtual screening? iScience 26, 105920. [doi]: http://dx.doi.org/10.1016/j.isci.2022.105920
• Cavasotto, CN, and Di Filippo, JI (2023). The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking. J. Chem. Inf. Model. 63, 2267-2280. [doi]: http://dx.doi.org/10.1021/acs.jcim.2c01471
  * Cavasotto, CN, and Scardino, V. (2022). Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point. ACS Omega 7, 47536-47546. [doi]: http://dx.doi.org/10.1021/acsomega.2c05693
• Villoutreix, BO, Cavasotto, CN, and Fernandez-Recio, J. (2022). Editorial: Development of COVID-19 therapies: Lessons learned and ongoing efforts. Front. Drug Discovery 2:1019705. [two].
• Scardino, V., Di Filippo, JI, and Cavasotto, CN (2022). How good are AlphaFold models for docking-based virtual screening? ChemRxiv. [two]
• Adler, NS, Cababie, LA, Sarto, C., Cavasotto, CN, Gebhard, LG, Estrin, DA, Gamarnik, AV, Arrar, M., and Kaufman, SB (2022). Insights into the product release mechanism of dengue virus NS3 helicase. Nucleic Acid Res. 50, 6968-6979. [two]
• Martinez, FA, Adler, NS, Cavasotto, CN, and Aucar, G. A. (2022). Solvent effects on the NMR shieldings of stacked DNA base pairsPhys. Chem. Chem. Phys.. 24, 18150-18160. [two]
• Di Filippo, JI, and Cavasotto, CN (2022). Guided structure-based ligand identification and design via artificial intelligence modeling. Expert Opin. Drug Discovery 17, 71-78. [two]
• Scardino, V., Bollini, M., and Cavasotto, CN (2021). Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds. RSC Advances 11, 35383-35391. [two]
• Gallo, G., Erdmann, E., and Cavasotto, CN (2021). Evaluation of Silicone Fluids and Resins as CO2 Thickeners for Enhanced Oil Recovery Using a Computational and Experimental Approach. ACS Omega 6, 24803-24813. [two]
• Di Filippo, JI, Bollini, M., and Cavasotto, CN (2021). A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier. Front. Chem. 9, 714678. [two]
• Cavasotto, CN, Lamas, M.S., and Maggini, J. (2021). Functional and druggability analysis of the SARS-CoV-2 proteome. Eur. J. Pharmacol. 890, 173705. [two]
• Cavasotto, CN, and Di Filippo, J. I. (2021). Artificial intelligence in the early stages of drug discovery. Arch. Biochem. Biophys. 698, 108730. [two]
• Cavasotto, CN, and Di Filippo, J. (2021). In silico Drug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Consensus Ranking. Mol. Inform. 40, e2000115. [two]
• Bayo, J., Fiore, EJ, Dominguez, LM, Cantero, MJ, Ciarlantini, MS, Malvicini, M., Atorrasagasti, C., Garcia, MG, Rossi, M., Cavasotto, CN, Martinez, E., Comin, J., and Mazzolini, G.D. (2021). Bioinformatic analysis of RHO family of GTPases identifies RAC1 pharmacological inhibition as a new therapeutic strategy for hepatocellular carcinoma. Expert 70, 1362-1374. [two]
• Lans, I., Palacio-Rodriguez, K., Cavasotto, CN, and Cossio, P. (2020). Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles. J. Comput-Aided. Mol. Des. 34, 1063-1077. [two]
• Cavasotto, CN and Aucar, MG (2020). High-throughput docking using quantum mechanical scoring. Front. Chem. 8, 246. [doi]
• Cavasotto, CN, and Di Filippo, JI (2020). in silico Drug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Quantum Mechanical Scoring. ChemRxiv. [two]
• Aucar, MG, and Cavasotto, CN (2020). Molecular Docking Using Quantum Mechanical-Based Methods. Methods Mol. Biol. 2114, 269-284. [pubmed] 
• Cavasotto, CN (2020). Binding Free Energy Calculation using Quantum Mechanics Aimed for Drug Lead Optimization. Methods Mol. Biol. 2114, 257-268. [pubmed] 
• Leal, ES, Adler, NS, Fernandez, GA, Gebhard, LG, Battini, L., Aucar, MG, Videla, M., Monge, ME, Hernandez De Los Rios, A., Acosta Davila, JA, Morell, ML, Cordo, SM, Garcia, CC, Gamarnik, AV, Cavasotto, CN, and Bollini, M. (2019). De novo design approaches targeting an envelope protein pocket to identify small molecules against dengue virus. Eur. J. Med. Chem. 182, 111628. [pubmed] [doi] 
• Palacio-Rodriguez, K., Lans, I., Cavasotto, CN, and Cossio, P. (2019). Exponential consensus ranking improves the outcome in docking and receiver ensemble docking. Sci. Rep. 9, 5142. [pubmed]  [doi]
• Cavasotto, CN, Aucar, M.G., and Adler, N.S. (2019). Computational chemistry in drug lead discovery and design. Int. J. Quantum Chem. 119, e25678. [doi]
• Szalai, AM, Armando, NG, Barabas, FM, Stefani, FD, Giordano, L., Bari, SE, Cavasotto, CN, Silberstein, S., and Aramendia, P.F. (2018). A fluorescence nanoscopy marker for corticotropin-releasing hormone type 1 receptor: computer design, synthesis, signaling effects, super-resolved fluorescence imaging, and in situ affinity constant in cells. Phys. Chem. Chem. Phys. 20, 29212-29220. [pubmed]  [doi]
• Pascual, MJ, Merwaiss, F., Leal, E., Quintana, ME, Capozzo, AV, Cavasotto, CN, Bollini, M., and Alvarez, DE (2018). Structure-based drug design for envelope protein E2 uncovers a new class of bovine viral diarrhea inhibitors that block virus entry. Antivirus. Res. 149, 179-190. [pubmed]  [doi]
• Jeong, Y.T., Simoneschi, D., Keegan, S., Melville, D., Adler, N.S., Saraf, A., Florens, L., Washburn, M.P., Cavasotto, CN, Fenyo, D., Cuervo, AM, Rossi, M., and Pagano, M. (2018). The ULK1-FBXW5-SEC23B nexus controls autophagy. eLife 7,e42253. [pubmed] [doi]
• Cavasotto, CN, Adler, NS, and Aucar, MG (2018). Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization. Front. chem. 6, 188. [pubmed] [doi]
• Bollini, M., Leal, ES, Adler, NS, Aucar, MG, Fernandez, GA, Pascual, MJ, Merwaiss, F., Alvarez, DE, and Cavasotto, CN (2018). Discovery of Novel Bovine Viral Diarrhea Inhibitors Using Structure-Based Virtual Screening on the Envelope Protein E2. Front. chem. 6, 79. [pubmed] [doi]
• Leal, ES, Aucar, MG, Gebhard, LG, Iglesias, NG, Pascual, MJ, Casal, JJ, Gamarnik, AV, Cavasotto, CN, and Bollini, M. (2017). Discovery of novel dengue virus entry inhibitors via a structure-based approach. Bioorg. Med. Chem. Lett. 27, 3851-3855. [pubmed] [doi]
• Echenique, P., Cavasotto, CN, De Marco, M., Garcia-Risueno, P., and Alonso, JL (2017). Correction: An Exact Expression to Calculate the Derivatives of Position-Dependent Observables in Molecular Simulations with Flexible Constraints. PLoS One 12,, e0189454. [pubmed] [doi]
• Lavecchia, MJ, Puig De La Bellacasa, R., Borrell, JI, and Cavasotto, CN (2016). Investigating molecular dynamics-guided lead optimization of EGFR inhibitors. Bioorg. Med. Chem. 24, 768-778. [pubmed]  [doi]
• Spyrakis, F., and Cavasotto, CN (2015). Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description. Arch. Biochem. Biophys. 583, 105-119. [pubmed] [doi]
• Spyrakis, F., and Cavasotto, CN (2015). «Incorporating Protein Flexibility in Structure-Based Drug Design,» in Lesson Learning from Medicinal Chemistry: In silico Food Science, ed. P. Cozzini. (Hauppauge, NY: Nova Science Publishers).
• Palomba, D., and Cavasotto, CN (2015). «Protein Structure Modeling in Drug Design,» in In Silico Drug Discovery and Design: Theory, Methods, Challenges, and Applications, ed. CN Cavasotto. (Boca Raton, FL: CRC Press, Taylor & Francis Group), 215-248.
• Cavasotto, CN, and Palomba, D. (2015). Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models. chem. Common. 51, 13576-13594. [pubmed]  [doi]
• Rossi, M., Rotblat, B., Ansell, K., Amelio, I., Caraglia, M., Misso, G., Bernassola, F., Cavasotto, CN, Knight, R.A., Ciechanover, A., and Melino, G. (2014). High throughput screening for inhibitors of the HECT ubiquitin E3 ligase ITCH identifies antidepressant drugs as regulators of autophagy. Cell Death Dis. 5, e1203. [pubmed]  [doi]
• Petrov, R.R., Knight, L., Chen, SR, Wager-Miller, J., Mcdaniel, S.W., Diaz, F., Barth, F., Pan, H.L., Mackie, K., Cavasotto, CN, and Diaz, P. (2013). Mastering tricyclic ring systems for desirable functional cannabinoid activity. eur. J.Med. Chem. 69, 881-907. [pubmed] [doi]
• Domenech, R., Hernandez-Cifre, JG, Bacarizo, J., Diez-Pena, AI, Martinez-Rodriguez, S., Cavasotto, CN, De La Torre, JG, Camara-Artigas, A., Velazquez-Campoy, A., and Neira, JL (2013). The Histidine-Phosphocarrier Protein of the Phosphoenolpyruvate: Sugar Phosphotransferase System of Bacillus sphaericus Self-Associates. PLoS One 8,e69307. [pubmed]  [doi]
• Brand, C.S., Hocker, H.J., Gorfe, A.A., Cavasotto, CN, and Dessauer, C.W. (2013). Isoform selectivity of adenylyl cyclase inhibitors: characterization of known and novel compounds. J. Pharmacol. Exp. Ther. 347, 265-275. [pubmed] [doi]
• He, W., Elizondo-Riojas, MA, Li, X., Lokesh, GL, Somasunderam, A., Thiviyanathan, V., Volk, DE, Durland, RH, Englehardt, J., Cavasotto, CN, and Gorenstein, D. G. (2012). X-aptamers: a bead-based selection method for random incorporation of druglike moieties onto next-generation aptamers for enhanced binding. Biochemistry 51, 8321-8323. [pubmed]  [doi]
• Gatica, EA, and Cavasotto, CN (2012). Ligand and Decoy Sets for Docking to G Protein-Coupled Receptors. J. Chem. Inf. Model. 52, 1-6. [pubmed]  [doi]
• Forti, F., Cavasotto, CN, Orozco, M., Barril, X., and Luque, FJ (2012). A Multilevel Strategy for the Exploration of the Conformational Flexibility of Small Molecules. J.Chem. Theory Comput. 8, 1808-1819. [pubmed]  [doi]
• Dasgupta, I., Tanifum, E.A., Srivastava, M., Phatak, S.S., Cavasotto, CN, Analoui, M., and Annapragada, A. (2012). Non inflammatory boronate based glucose-responsive insulin delivery systems. PLoS One 7, e29585. [pubmed]  [doi]
• Cavasotto, CN (2012). Normal mode-based approaches in receiver ensemble docking. Methods Mol. Biol. 819, 157-168. [pubmed] [doi]
• Cavasotto, CN (2012). «Binding free energy calculations and scoring in small-molecule docking,» in Physico-Chemical and Computational Approaches to Drug Discovery, eds.  FJ Luque & X. Barrel. (London: Royal Society of Chemistry), 195-222.
• Vilar, S., Ferino, G., Phatak, S.S., Berk, B., Cavasotto, CN, and Costanzi, S. (2011). Docking-based virtual screening for ligands of G protein-coupled receptors: not only crystal structures but also in silico models. J. Mol. Graph. Model. 29, 614-623. [pubmed]  [doi]
• Echenique, P., Cavasotto, CN, and García-Risueño, P. (2011). The canonical equilibrium of constrained molecular models. eur. Phys. J. Special Topics 200, 5-54. [doi]
• Echenique, P., Cavasotto, CN, De Marco, M., Garca-Risueño, P., and Alonso, JL (2011). An exact expression to calculate the derivatives of position-dependent observables in molecular simulations with flexible constraints. PLoS One 6, e24563. [pubmed]  [doi]
• Cavasotto, CN, and Phatak, S.S. (2011). Docking methods for structure-based library design. Methods Mol. Biol. 685, 155-174. [pubmed]  [doi]
• Cavasotto, CN (2011). Homology models in docking and high-throughput docking. Curr. Top. Med. Chem. 11, 1528-1534. [pubmed]  [doi]
• Cavasotto, CN (2011). “Handling protein flexibility in docking and high-throughput docking,” in Virtual Screening. Principles, Challenges and Practical Guidelines, ed. C. Sotriffer. (Weinheim, Germany: Wiley-VCH Verlag), 245-262.
• Bocanegra, R., Nevot, M., Domenech, R., Lopez, I., Abian, O., Rodriguez-Huete, A., Cavasotto, CN, Velazquez-Campoy, A., Gomez, J., Martinez, MA, Neira, JL, and Mateu, MG (2011). Rationally designed interfacial peptides are efficient in vitro inhibitors of HIV-1 capsid assembly with antiviral activity. PLoS One 6, e23877. [pubmed]  [doi]
• Anisimov, V.M., Ziemys, A., Kizhake, S., Yuan, Z., Natarajan, A., and Cavasotto, CN (2011). Computational and experimental studies of the interaction between phospho-peptides and the C-terminal domain of BRCA1. J.Comput. Aided Mol. Des. 25, 1071-1084. [pubmed]  [doi]
• Anisimov, VM, and Cavasotto, CN (2011). Quantum mechanical binding free energy calculation for phosphopeptide inhibitors of the Lck SH2 domain. J. Comput. Chem. 32, 2254-2263. [pubmed]  [doi]
• Anisimov, VM, and Cavasotto, CN (2011). Hydration free energies using semiempirical quantum mechanical Hamiltonians and a continuum solvent model with multiple atomic-type parameters. J. Phys. Chem. B 115, 7896-7905. [pubmed] [doi]
• Phatak, SS, Gatica, EA, and Cavasotto, CN (2010). Ligand-steered modeling and docking: A benchmarking study in Class A G-Protein-Coupled Receptors. J. Chem. Inf. Model. 50, 2119-2128. [pubmed]  [doi]
• Anisimov, VM, and Cavasotto, CN (2010). “Quantum-Mechanical Molecular Dynamics of Charge Transfer,” in Kinetics and Dynamics, eds. P. Paneth & A. Dybala-Defratyka. Springer Netherlands), 247-266.
• Ziemys, A., Ferrari, M., and Cavasotto, CN (2009). Molecular modeling of glucose diffusivity in silica nanochannels. J. Nanosci. Nanotechnol. 9, 6349-6359. [pubmed]
• Phatak, SS, Stephan, CC, and Cavasotto, CN (2009). High-throughput and in silico screenings in drug discovery. exp. Opin. Drug Discovery 4, 947-959. [pubmed] [doi]
• Monti, MC, Casapullo, A., Cavasotto, CN, Tosco, A., Dal Piaz, F., Ziemys, A., Margarucci, L., and Riccio, R. (2009). The binding mode of petrosaspongiolide M to the human group IIA phospholipase A(2): exploring the role of covalent and noncovalent interactions in the inhibition process. Chem.-Eur. J. 15, 1155-1163. [pubmed]
• Diaz, P., Phatak, SS, Xu, J., Fronczek, FR, Astruc-Diaz, F., Thompson, CM, Cavasotto, CN, and Naguib, M. (2009). 2,3-Dihydro-1-benzofuran derivatives as a series of potent selective cannabinoid receptor 2 agonists: design, synthesis, and binding mode prediction through ligand-steered modeling. ChemMedChem 4, 1615-1629. [pubmed]
• Diaz, P., Phatak, S.S., Xu, J., Astruc-Diaz, F., Cavasotto, CN, and Naguib, M. (2009). 6-Methoxy-N-alkyl isatin acylhydrazone derivatives as a novel series of potent selective cannabinoid receptor 2 inverse agonists: Design, Synthesis and Binding Mode Prediction. J. Med. Chem. 52, 433-444. [pubmed]
• Cavasotto, CN, and Phatak, S.S. (2009). Homology modeling in drug discovery: current trends and applications. Drug Discovery Today 14, 676-683. [pubmed]
• Anisimov, V.M., Bugaenko, V.L., and Cavasotto, CN (2009). Quantum mechanical dynamics of charge transfer in ubiquitin in aqueous solution. ChemPhysChem 10, 3194-3196. [pubmed]
• Torra, IP, Ismaili, N., Feig, JE, Xu, CF, Cavasotto, CN, Pancratov, R., Rogatsky, I., Neubert, T.A., Fisher, E.A., and Garabedian, M.J. (2008). Phosphorylation of liver X receptor alpha selectively regulates target gene expression in macrophages. mol. Cell. Biol. 28, 2626-2636. [pubmed]
• Chen, W., Dang, T., Blind, R.D., Wang, Z., Cavasotto, CN, Hittelman, A.B., Rogatsky, I., Logan, S.K., and Garabedian, M.J. (2008). Glucocorticoid receptor phosphorylation differentially affects target gene expression. mol. Endocrinol. 22, 1754-1766. [pubmed]
• Cavasotto, CN, and Singh, N. (2008). Docking and High Throughput Docking: Successes and the Challenge of Protein Flexibility. Comput-Aided Drug Design 4, 221-234. [doi]
• Cavasotto, CN, Orry, AJ, Murgolo, NJ, Czarniecki, MF, Kocsi, SA, Hawes, BE, O'neill, KA, Hine, H., Burton, MS, Voigt, JH, Abagyan, RA, Bayne, ML, and Monsma, FJ, Jr. (2008). Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening. J.Med. Chem. 51, 581-588. [pubmed]
• Bisson, WH, Abagyan, R., and Cavasotto, CN (2008). Molecular basis of agonicity and antagonicity in the androgen receptor studied by molecular dynamics simulations. J. Mol. Graphics Modell. 27, 452-458. [pubmed]
• Monti, MC, Casapullo, A., Cavasotto, CN, Napolitano, A., and Riccio, R. (2007). Scalaradial, a Dialdehyde-Containing Marine Metabolite That Causes an Unexpected Noncovalent PLA(2) Inactivation. ChemBioChem 8, 1585-1591. [pubmed]
• Cavasotto, CN, and Orry, A.J. (2007). Ligand Docking and Structure-based Virtual Screening in Drug Discovery. curr. Top. Med. Chem. 7, 1006-1014. [pubmed]
• Orry, AJ, Abagyan, RA, and Cavasotto, CN (2006). Structure-based development of target-specific compound libraries. Drug Discovery Today 11, 261-266. [pubmed]
• Cavasotto, CN, Ortiz, MA, Abagyan, RA, and Piedrafita, FJ (2006). In silico identification of novel EGFR inhibitors with antiproliferative activity against cancer cells. Bioorg. Med. Chem. Lett. 16, 1969-1974. [pubmed]
• Cavasotto, CN, Orry, A.J.W., and Abagyan, R. (2006). «Receiver Flexibility in Ligand Docking,» in Handbook of Theoretical and Computational Nanotechnology, M. Rieth & W. Schommers. American Scientific Publishers), 218-257.
• Cavasotto, CN (2006). Ligand Docking and Virtual Screening in Structure‐based Drug Discovery. AIP Confidence Proc. 851, 34-49. [doi]
• Li, W., Cavasotto, CN, Cardozo, T., Ha, S., Dang, T., Taneja, SS, Logan, SK, and Garabedian, MJ (2005). Androgen receptor mutations identified in prostate cancer and androgen insensitivity syndrome display aberrant ART-27 coactivator function. Mol. Endocrinol. 19, 2273-2282. [pubmed]
• Kovacs, JA, Cavasotto, CN, and Abagyan, R. A. (2005). Conformational Sampling of Protein Flexibility in Generalized Coordinates: Application to ligand docking. J. Comp. Theor. Nanosci. 2, 354-361. [doi]
• Hernandez, JA, Meier, J., Barrera, FN, De Los Panos, OR, Hurtado-Gomez, E., Bes, MT, Fillat, MF, Peleato, ML, Cavasotto, CN, and Neira, J.L. (2005). The conformational stability and thermodynamics of Fur A (ferric uptake regulator) from Anabaena sp. PCC 7119. Biophysics J. 89, 4188-4200. [pubmed]
• Cavasotto, CN, Orry, A.J.W., and Abagyan, R.A. (2005). The Challenge of Considering Receiver Flexibility in Ligand Docking and Virtual Screening. Curr. Comput.-Aided Drug Des. 1, 423-440. [doi]
• Cavasotto, CN, Kovacs, J. A., and Abagyan, R. A. (2005). Representing Receiver Flexibility in Ligand Docking through Relevant Normal Modes. J. Am. Chem. Soc. 127, 9632-9640. [pubmed] [doi]
• Cavasotto, CN, Liu, G., James, SY, Hobbs, PD, Peterson, VJ, Bhattacharya, AA, Kolluri, SK, Zhang, XK, Leid, M., Abagyan, R., Liddington, RC, and Dawson, MI (2004). Determinants of retinoid X receptor transcriptional antagonism. J. Med. Chem. 47, 4360-4372. [pubmed]
• Cavasotto, CN, and Abagyan, R. A. (2004). Protein flexibility in ligand docking and virtual screening to protein kinasesJ. Mol. Biol. 337, 209-225.
• Giribet, CG, De Azúa, MCR, Vizioli, CV, and Cavasotto, CN (2003). Electronic mechanisms of intra and intermolecular J couplings in systems with CH…O interactions. Int. J. Mol. Sci. 4, 203-217. [doi]
• Cavasotto, CN, Orry, A.J., and Abagyan, R.A. (2003). Structure-based identification of binding sites, native ligands and potential inhibitors for G-protein coupled receptors. Proteins 51, 423-433. [pubmed]
• Bordner, AJ, Cavasotto, CN, and Abagyan, R. A. (2003). Direct derivation of van der Waals force field parameters from quantum mechanical interaction energies. J. Phys. Chem. B 107, 9601-9609. [doi]
• Bordner, AJ, Cavasotto, CN, and Abagyan, R. A. (2002). Accurate Transferable Model for Water, n-Octanol, and n-Hexadecane Solvation Free Energies. J.Phys. Chem. B 106, 11009-11015. [doi]
• Cavasotto, CN (2000). Finite expansion of the inverse matrix in the polarization propagator method. Theo. Chem. Acc. 104, 491-498. [doi]
• Cavasotto, CN, and Grinberg, H. (1999). A Liouville-space method for the decoupling of the polarization propagator equation of motion. Chem. Phys. Lett. 303, 558-566. [doi]
• Bochicchio, R.C., Ferraro, M.B., Grinberg, H., and Cavasotto, CN (1995). Self-Energies for the Particle-Hole Propagator from Feynman-Dyson Equations. J. Mol. Struct. (THEOCHEM) 335, 1-9. [doi]
• Cavasotto, CN, Giribet, CG, De Azúa, MCR, and Contreras, RH (1991). Exo-Exo and Endo-Endo Vicinal Proton Spin-Spin Coupling-Constants in Norbornane and Norbornene – an IPPP-CLOPPA Analysis. J.Comput. Chem. 12, 141-146. [doi]
• Contreras, RH, Giribet, CG, De Azúa, MCR, Cavasotto, CN, Aucar, GA, and Krivdin, LB (1990). Quantum Chemical-Analysis of the Orientational Lone-Pair Effect on Spin-Spin Coupling-Constants. J. Mol. Struct. (THEOCHEM) 69, 175-186. [doi]
• Cavasotto, CN, Giribet, CG, De Azúa, MCR, Contreras, RH, and Pérez, JE (1990). 2J(Se-Se) Couplings in Diseleno-Substituted Alkenylic Compounds – a CLOPPA-IPPPAnalysis. J. Mag. Reson. 87, 209-219. [doi]
• Cavasotto, CN, Giribet, CG, and Contreras, RH (1990). Localization Method for Semiempirical Molecular-Orbitals Based on the Boys-Foster Criterion. J. Mol. Struct. (THEOCHEM) 69, 107-110. [doi]

• In Silico Drug Discovery and Design: Theory, Methods, Challenges, and Applications. Ed. C. Cavasotto, CRC Press, Taylor & Francis Group, Boca Raton, FL, 2015; 558 pp, ISBN 978-1-4822-1783-4.

Dr. Claudio N. Cavasotto, Principal Investigator CONICET, CCavasotto@austral.edu.ar

Eng. Valeria Scardino, Doctoral thesis, VScardino@austral.edu.ar

PICT-2021-1129, "Advanced computational methods based on machine learning for drug discovery: Development and application for the search for antivirals"