Zobrazeno 1 - 10
of 205
pro vyhledávání: '"Edwards, Carl"'
Autor:
Edwards, Carl, Lu, Ziqing, Hajiramezanali, Ehsan, Biancalani, Tommaso, Ji, Heng, Scalia, Gabriele
Bridging biomolecular modeling with natural language information, particularly through large language models (LLMs), has recently emerged as a promising interdisciplinary research area. LLMs, having been trained on large corpora of scientific documen
Externí odkaz:
http://arxiv.org/abs/2411.00737
Autor:
Li, Xiner, Wang, Limei, Luo, Youzhi, Edwards, Carl, Gui, Shurui, Lin, Yuchao, Ji, Heng, Ji, Shuiwang
We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries. Although tokenization of molecular graphs exists, that for 3D geometries is largely unexplored. Here, we attempt
Externí odkaz:
http://arxiv.org/abs/2408.10120
This paper presents a novel approach for predicting Power Conversion Efficiency (PCE) of Organic Photovoltaic (OPV) devices, called GLaD: synergizing molecular Graphs and Language Descriptors for enhanced PCE prediction. Due to the lack of high-quali
Externí odkaz:
http://arxiv.org/abs/2405.14203
Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released
Externí odkaz:
http://arxiv.org/abs/2403.00791
Autor:
Sprueill, Henry W., Edwards, Carl, Agarwal, Khushbu, Olarte, Mariefel V., Sanyal, Udishnu, Johnston, Conrad, Liu, Hongbin, Ji, Heng, Choudhury, Sutanay
The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with qua
Externí odkaz:
http://arxiv.org/abs/2402.10980
Autor:
Li, Sha, Han, Chi, Yu, Pengfei, Edwards, Carl, Li, Manling, Wang, Xingyao, Fung, Yi R., Yu, Charles, Tetreault, Joel R., Hovy, Eduard H., Ji, Heng
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history. This has resulted in concerns that the f
Externí odkaz:
http://arxiv.org/abs/2310.20633
Autor:
Sprueill, Henry W., Edwards, Carl, Olarte, Mariefel V., Sanyal, Udishnu, Ji, Heng, Choudhury, Sutanay
Publikováno v:
In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP2023) Findings
Discovering novel catalysts requires complex reasoning involving multiple chemical properties and resultant trade-offs, leading to a combinatorial growth in the search space. While large language models (LLM) have demonstrated novel capabilities for
Externí odkaz:
http://arxiv.org/abs/2310.14420
Autor:
Zhang, Xuan, Wang, Limei, Helwig, Jacob, Luo, Youzhi, Fu, Cong, Xie, Yaochen, Liu, Meng, Lin, Yuchao, Xu, Zhao, Yan, Keqiang, Adams, Keir, Weiler, Maurice, Li, Xiner, Fu, Tianfan, Wang, Yucheng, Yu, Haiyang, Xie, YuQing, Fu, Xiang, Strasser, Alex, Xu, Shenglong, Liu, Yi, Du, Yuanqi, Saxton, Alexandra, Ling, Hongyi, Lawrence, Hannah, Stärk, Hannes, Gui, Shurui, Edwards, Carl, Gao, Nicholas, Ladera, Adriana, Wu, Tailin, Hofgard, Elyssa F., Tehrani, Aria Mansouri, Wang, Rui, Daigavane, Ameya, Bohde, Montgomery, Kurtin, Jerry, Huang, Qian, Phung, Tuong, Xu, Minkai, Joshi, Chaitanya K., Mathis, Simon V., Azizzadenesheli, Kamyar, Fang, Ada, Aspuru-Guzik, Alán, Bekkers, Erik, Bronstein, Michael, Zitnik, Marinka, Anandkumar, Anima, Ermon, Stefano, Liò, Pietro, Yu, Rose, Günnemann, Stephan, Leskovec, Jure, Ji, Heng, Sun, Jimeng, Barzilay, Regina, Jaakkola, Tommi, Coley, Connor W., Qian, Xiaoning, Qian, Xiaofeng, Smidt, Tess, Ji, Shuiwang
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range
Externí odkaz:
http://arxiv.org/abs/2307.08423
Predicting synergistic drug combinations can help accelerate discovery of cancer treatments, particularly therapies personalized to a patient's specific tumor via biopsied cells. In this paper, we propose a novel setting and models for in-context dru
Externí odkaz:
http://arxiv.org/abs/2307.11694
We present $\textbf{MolT5}$ $-$ a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings. $\textbf{MolT5}$ allows for new, useful, and challenging analogs of traditional visi
Externí odkaz:
http://arxiv.org/abs/2204.11817