Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Authors: Zimmermann, Yoel, Bazgir, Adib, Afzal, Zartashia, Agbere, Fariha, Ai, Qianxiang, Alampara, Nawaf, Al-Feghali, Alexander, Ansari, Mehrad, Antypov, Dmytro, Aswad, Amro, Bai, Jiaru, Baibakova, Viktoriia, Biswajeet, Devi Dutta, Bitzek, Erik, Bocarsly, Joshua D., Borisova, Anna, Bran, Andres M, Brinson, L. Catherine, Calderon, Marcel Moran, Canalicchio, Alessandro, Chen, Victor, Chiang, Yuan, Circi, Defne, Charmes, Benjamin, Chaudhary, Vikrant, Chen, Zizhang, Chiu, Min-Hsueh, Clymo, Judith, Dabhadkar, Kedar, Daelman, Nathan, Datar, Archit, de Jong, Wibe A., Evans, Matthew L., Fard, Maryam Ghazizade, Fisicaro, Giuseppe, Gangan, Abhijeet Sadashiv, George, Janine, Gonzalez, Jose D. Cojal, Götte, Michael, Gupta, Ankur K., Harb, Hassan, Hong, Pengyu, Ibrahim, Abdelrahman, Ilyas, Ahmed, Imran, Alishba, Ishimwe, Kevin, Issa, Ramsey, Jablonka, Kevin Maik, Jones, Colin, Josephson, Tyler R., Juhasz, Greg, Kapoor, Sarthak, Kang, Rongda, Khalighinejad, Ghazal, Khan, Sartaaj, Klawohn, Sascha, Kuman, Suneel, Ladines, Alvin Noe, Leang, Sarom, Lederbauer, Magdalena, Sheng-Lun, Liao, Liu, Hao, Liu, Xuefeng, Lo, Stanley, Madireddy, Sandeep, Maharana, Piyush Ranjan, Maheshwari, Shagun, Mahjoubi, Soroush, Márquez, José A., Mills, Rob, Mohanty, Trupti, Mohr, Bernadette, Moosavi, Seyed Mohamad, Moßhammer, Alexander, Naghdi, Amirhossein D., Naik, Aakash, Narykov, Oleksandr, Näsström, Hampus, Nguyen, Xuan Vu, Ni, Xinyi, O'Connor, Dana, Olayiwola, Teslim, Ottomano, Federico, Ozhan, Aleyna Beste, Pagel, Sebastian, Parida, Chiku, Park, Jaehee, Patel, Vraj, Patyukova, Elena, Petersen, Martin Hoffmann, Pinto, Luis, Pizarro, José M., Plessers, Dieter, Pradhan, Tapashree, Pratiush, Utkarsh, Puli, Charishma, Qin, Andrew, Rajabi, Mahyar, Ricci, Francesco, Risch, Elliot, Ríos-García, Martiño, Roy, Aritra, Rug, Tehseen, Sayeed, Hasan M, Scheidgen, Markus, Schilling-Wilhelmi, Mara, Schloz, Marcel, Schöppach, Fabian, Schumann, Julia, Schwaller, Philippe, Schwarting, Marcus, Sharlin, Samiha, Shen, Kevin, Shi, Jiale, Si, Pradip, D'Souza, Jennifer, Sparks, Taylor, Sudhakar, Suraj, Talirz, Leopold, Tang, Dandan, Taran, Olga, Terboven, Carla, Tropin, Mark, Tsymbal, Anastasiia, Ueltzen, Katharina, Unzueta, Pablo Andres, Vasan, Archit, Vinchurkar, Tirtha, Vo, Trung, Vogel, Gabriel, Völker, Christoph, Weinreich, Jan, Yang, Faradawn, Zaki, Mohd, Zhang, Chi, Zhang, Sylvester, Zhang, Weijie, Zhu, Ruijie, Zhu, Shang, Janssen, Jan, Li, Calvin, Foster, Ian, Blaiszik, Ben

Subject Terms: Computer Science - Machine Learning, Condensed Matter - Materials Science, Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2411.15221

Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data

Authors: Overbeek, Jamie C., Partin, Alexander, Brettin, Thomas S., Chia, Nicholas, Narykov, Oleksandr, Vasanthakumari, Priyanka, Wilke, Andreas, Zhu, Yitan, Clyde, Austin, Jones, Sara, Gnanaolivu, Rohan, Liu, Yuanhang, Jiang, Jun, Wang, Chen, Knutson, Carter, McNaughton, Andrew, Kumar, Neeraj, Fernando, Gayara Demini, Ghosh, Souparno, Sanchez-Villalobos, Cesar, Zhang, Ruibo, Pal, Ranadip, Weil, M. Ryan, Stevens, Rick L.

Subject Terms: Quantitative Biology - Biomolecules, Computer Science - Machine Learning

Access URL: http://arxiv.org/abs/2409.12215

Influencing factors on false positive rates when classifying tumor cell line response to drug treatment

Authors: Vasanthakumari, Priyanka, Brettin, Thomas, Zhu, Yitan, Yoo, Hyunseung, Shukla, Maulik, Partin, Alexander, Xia, Fangfang, Narykov, Oleksandr, Stevens, Rick L.

Subject Terms: Quantitative Biology - Quantitative Methods

Access URL: http://arxiv.org/abs/2310.11329

Deep learning methods for drug response prediction in cancer: predominant and emerging trends

Authors: Partin, Alexander, Brettin, Thomas S., Zhu, Yitan, Narykov, Oleksandr, Clyde, Austin, Overbeek, Jamie, Stevens, Rick L.

Subject Terms: Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning

Access URL: http://arxiv.org/abs/2211.10442

Molecular architecture and dynamics of SARS-CoV-2 envelope by integrative modeling

Authors: Pezeshkian, Weria, Grünewald, Fabian, Narykov, Oleksandr, Lu, Senbao, Arkhipova, Valeria, Solodovnikov, Alexey, Wassenaar, Tsjerk A., Marrink, Siewert J., Korkin, Dmitry

Source: In Structure 6 April 2023 31(4):492-503

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models.

Authors: Narykov, Oleksandr¹ (AUTHOR) onarykov@anl.gov, Zhu, Yitan¹ (AUTHOR) brettin@anl.gov, Brettin, Thomas¹ (AUTHOR) apartin@anl.gov, Evrard, Yvonne A.² (AUTHOR) evrardy@mail.nih.gov, Partin, Alexander¹ (AUTHOR) mshukla@anl.gov, Shukla, Maulik¹ (AUTHOR) fangfang@anl.gov, Xia, Fangfang¹ (AUTHOR) pvasanthakumari@anl.gov, Clyde, Austin^1,3 (AUTHOR) stevens@anl.gov, Vasanthakumari, Priyanka¹ (AUTHOR), Doroshow, James H.⁴ (AUTHOR) doroshoj@mail.nih.gov, Stevens, Rick L.^1,3 (AUTHOR)

Source: Cancers. Jan2024, Vol. 16 Issue 1, p50. 19p.

Subject Terms: *COMPUTER simulation, *DEEP learning, *CLINICAL drug trials, *ANTINEOPLASTIC agents, *MACHINE learning, *GENE expression, *PREDICTION models

Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

Authors: Narykov, Oleksandr, Johnson, Nathan T., Korkin, Dmitry

Source: In Cell Reports 23 November 2021 37(8)

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening.

Authors: Vasanthakumari, Priyanka, Zhu, Yitan, Brettin, Thomas, Partin, Alexander, Shukla, Maulik, Xia, Fangfang, Narykov, Oleksandr, Weil, Michael Ryan, Stevens, Rick L.

Source: Cancers; Feb2024, Vol. 16 Issue 3, p530, 18p

Subject Terms: THERAPEUTIC use of antineoplastic agents, CLINICAL drug trials, DRUG discovery, MACHINE learning, LEARNING strategies, RESEARCH funding, DRUG development, TUMORS, PREDICTION models

Single-cell long-read sequencing-based mapping reveals specialized splicing patterns in developing and adult mouse and human brain

Authors: Joglekar, Anoushka, Hu, Wen, Zhang, Bei, Narykov, Oleksandr, Diekhans, Mark, Marrocco, Jordan, Balacco, Jennifer, Ndhlovu, Lishomwa C., Milner, Teresa A., Fedrigo, Olivier, Jarvis, Erich D., Sheynkman, Gloria, Korkin, Dmitry, Ross, M. Elizabeth, Tilgner, Hagen U.

Source: Nature Neuroscience; 20240101, Issue: Preprints p1-13, 13p

DISPOT: a simple knowledge-based protein domain interaction statistical potential.

Authors: Narykov, Oleksandr¹ (AUTHOR) onarykov@wpi.edu, Bogatov, Dmytro² (AUTHOR), Korkin, Dmitry^1,3 (AUTHOR) onarykov@wpi.edu

Source: Bioinformatics. 12/15/2019, Vol. 35 Issue 24, p5374-5378. 5p.

Subject Terms: *PROTEIN domains, *INTERNET servers, *PROTEIN-protein interactions, *CYTOSKELETAL proteins, *SOURCE code, *PYTHON programming language

Computational protein modeling and the next viral pandemic

Authors: Narykov, Oleksandr, Srinivasan, Suhas, Korkin, Dmitry

Source: Nature Methods; May 2021, Vol. 18 Issue: 5 p444-445, 2p

Structural Genomics of SARS-CoV-2 Indicates Evolutionary Conserved Functional Regions of Viral Proteins.

Authors: Srinivasan, Suhas, Cui, Hongzhu, Gao, Ziyang, Liu, Ming, Lu, Senbao, Mkandawire, Winnie, Narykov, Oleksandr, Sun, Mo, Korkin, Dmitry

Source: Viruses (1999-4915); Apr2020, Vol. 12 Issue 4, p360-360, 1p

Subject Terms: COVID-19, VIRAL proteins, SARS virus, VIRUS diseases, GENOMICS

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