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\n  \n 2024\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n A New Ultra-High-Throughput Assay for Measuring Protein Fitness.\n \n \n \n \n\n\n \n Sundar, V.; Tu, B.; Guan, L.; and Esvelt, K.\n\n\n \n\n\n\n 2024.\n Oral presentation\n\n\n\n
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@conference{flightedgem,\n    author = "Vikram Sundar and Boqiang Tu and Lindsey Guan and Kevin Esvelt",\n    title = {{A New Ultra-High-Throughput Assay for Measuring Protein Fitness}},\n    booktitle = "ICLR Workshop: Generative and Experimental Perspectives for Biomolecular Design",\n    year = "2024",\n    note = "Oral presentation",\n    url_Paper = {https://openreview.net/attachment?id=UjcdHtZptE&name=pdf},\n}\n\n
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\n \n\n \n \n \n \n \n \n An ultra-high-throughput method for measuring biomolecular activities.\n \n \n \n \n\n\n \n Tu, B.; Sundar, V.; and Esvelt, K.\n\n\n \n\n\n\n bioRxiv. 2024.\n \n\n\n\n
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@article{dharma,\n    author = "Boqiang Tu and Vikram Sundar and Kevin Esvelt",\n    title = {{An ultra-high-throughput method for measuring biomolecular activities}},\n    journal = "bioRxiv",\n    year = "2024",\n    url_Paper={https://www.biorxiv.org/content/10.1101/2022.03.09.483646v4},\n}\n\n
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\n \n\n \n \n \n \n \n \n FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data.\n \n \n \n \n\n\n \n Sundar, V.; Tu, B.; Guan, L.; and Esvelt, K.\n\n\n \n\n\n\n bioRxiv. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"FLIGHTED: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{flightedmain,\n    author = "Vikram Sundar and Boqiang Tu and Lindsey Guan and Kevin Esvelt",\n    title = {{FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data}},\n    journal = "bioRxiv",\n    year = "2024",\n    url_Paper={https://www.biorxiv.org/content/10.1101/2024.03.26.586797v1},\n}
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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Data.\n \n \n \n \n\n\n \n Sundar, V.; Tu, B.; Guan, L.; and Esvelt, K.\n\n\n \n\n\n\n 2023.\n \n\n\n\n
\n\n\n\n \n \n \"FLIGHTED: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@conference{flightedconf,\n    author = "Vikram Sundar and Boqiang Tu and Lindsey Guan and Kevin Esvelt",\n    title = {{FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Data}},\n    booktitle = "NeurIPS Workshop: Machine Learning and Structural Biology",\n    year = "2023",\n    url_Paper={https://www.mlsb.io/papers_2023/FLIGHTED_Inferring_Fitness_Landscapes_from_Noisy_High-Throughput_Experimental_Data.pdf},\n}\n\n
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\n  \n 2022\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Neural Network-Derived Potts Models for Structure-Based Protein Design using Backbone Atomic Coordinates and Tertiary Motifs.\n \n \n \n \n\n\n \n Li, A.; Lu, M.; Desta, I.; Sundar, V.; Grigoryan, G.; and Keating, A.\n\n\n \n\n\n\n Protein Science, 32: e4554. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"Neural paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{TERMinator,\n    author = "Alex Li and Mindren Lu and Israel Desta and Vikram Sundar and Gevorg Grigoryan and Amy Keating",\n\ttitle = {{Neural Network-Derived Potts Models for Structure-Based Protein Design using Backbone Atomic Coordinates and Tertiary Motifs}},\n\tyear = {2022},\n\tjournal = {Protein Science},\n    volume = {32},\n    issue = {2},\n    pages = {e4554},\n    url_Paper={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854172/pdf/PRO-32-e4554.pdf},\n}\n\n
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\n  \n 2021\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs.\n \n \n \n \n\n\n \n Li, A.; Sundar, V.; Grigoryan, G.; and Keating, A.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
\n\n\n\n \n \n \"TERMinator: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@conference{TERMinatorconf,\n    author = "Alex Li and Vikram Sundar and Gevorg Grigoryan and Amy Keating",\n    title = "{TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs}",\n    booktitle = "NeurIPS Workshop: Machine Learning and Structural Biology",\n    year = "2021",\n    url_Paper={https://arxiv.org/pdf/2204.13048.pdf},\n}\n\n
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\n  \n 2020\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n The Effect of Debiasing Protein-Ligand Binding Data on Generalization.\n \n \n \n \n\n\n \n Sundar, V.; and Colwell, L.\n\n\n \n\n\n\n J Chem Inf Model, 60(1): 56-62. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"The paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{debiasing,\n    author="Vikram Sundar and Lucy Colwell",\n    title="{The Effect of Debiasing Protein-Ligand Binding Data on Generalization}",\n    year="2020",\n    journal="J Chem Inf Model",\n    number="1",\n    volume="60",\n    pages="56-62",\n    url_Paper={https://pubs.acs.org/doi/10.1021/acs.jcim.9b00415},\n}\n\n
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\n \n\n \n \n \n \n \n \n Using Single Protein/Ligand Binding Models to Predict Active Ligands for Unseen Proteins.\n \n \n \n \n\n\n \n Sundar, V.; and Colwell, L.\n\n\n \n\n\n\n bioRxiv. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Using paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{DTI,\n    author="Vikram Sundar and Lucy Colwell",\n    title="{Using Single Protein/Ligand Binding Models to Predict Active Ligands for Unseen Proteins}",\n    journal="bioRxiv",\n    year="2020",\n    url_Paper={https://www.biorxiv.org/content/10.1101/2020.08.02.233155v2.full.pdf},\n}\n\n
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\n \n\n \n \n \n \n \n \n Attribution Methods Reveal Flaws in Fingerprint-Based Virtual Screening.\n \n \n \n \n\n\n \n Sundar, V.; and Colwell, L.\n\n\n \n\n\n\n 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Attribution paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@conference{attributionconf,\n    author = "Vikram Sundar and Lucy Colwell",\n    title = "{Attribution Methods Reveal Flaws in Fingerprint-Based Virtual Screening}",\n    booktitle = "ICML Workshop: ML Interpretability for Scientific Discovery",\n    year = "2020",\n    url_Paper={https://arxiv.org/pdf/2007.01436.pdf},\n}\n\n
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\n  \n 2019\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n Using Machine Learning to Predict Protein/Ligand Interactions.\n \n \n \n\n\n \n Sundar, V.\n\n\n \n\n\n\n MPhil Thesis, University of Cambridge, 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@mastersthesis{mphilthesis,\n    author="Vikram Sundar",\n    title="{Using Machine Learning to Predict Protein/Ligand Interactions}",\n    year="2019",\n    school="University of Cambridge",\n    type="{MPhil Thesis}"\n}\n\n
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\n \n\n \n \n \n \n \n \n Using Single Protein/Ligand Binding Models to Predict Active Ligands for Previously Unseen Proteins.\n \n \n \n \n\n\n \n Sundar, V.; and Colwell, L.\n\n\n \n\n\n\n 2019.\n \n\n\n\n
\n\n\n\n \n \n \"Using paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@conference{DTIconf,\n    author = "Vikram Sundar and Lucy Colwell",\n    title = "{Using Single Protein/Ligand Binding Models to Predict Active Ligands for Previously Unseen Proteins}",\n    booktitle = "NeurIPS Workshop: Machine Learning and the Physical Sciences",\n    year = "2019",\n    url_Paper={https://ml4physicalsciences.github.io/2019/files/NeurIPS_ML4PS_2019_66.pdf},\n}\n\n
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\n \n\n \n \n \n \n \n \n Reproducing Quantum Probability Distributions at the Speed of Classical Dynamics: A New Approach for Developing Force-Field Functors.\n \n \n \n \n\n\n \n Sundar, V.; Gelbwaser-Klimovsky, D.; and Aspuru-Guzik, A.\n\n\n \n\n\n\n J Phys Chem Lett, 9(7): 1721-1727. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Reproducing paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{ffftheory,\n    author="Vikram Sundar and David Gelbwaser-Klimovsky and Al\\'an Aspuru-Guzik",\n    title="{Reproducing Quantum Probability Distributions at the Speed of Classical Dynamics: A New Approach for Developing Force-Field Functors}",\n    year="2018",\n    journal="J Phys Chem Lett",\n    number="7",\n    volume="9",\n    pages="1721-1727",\n    url_Paper={https://pubs.acs.org/doi/10.1021/acs.jpclett.7b03254},\n}\n\n
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\n \n\n \n \n \n \n \n \n Bounds on Errors in Observables Computed from Molecular Dynamics Simulations.\n \n \n \n \n\n\n \n Sundar, V.\n\n\n \n\n\n\n Senior Thesis, Harvard University, 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Bounds paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@mastersthesis{seniorthesis,\n    author="Vikram Sundar",\n    title="{Bounds on Errors in Observables Computed from Molecular Dynamics Simulations}",\n    school="Harvard University",\n    year="2018",\n    type="{Senior Thesis}",\n    url_Paper={https://legacy-www.math.harvard.edu/theses/senior/sundar/sundar.pdf},\n}\n\n
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