Results 11 to 20 of about 25,447,146 (290)
ChemBERTa-2: Towards Chemical Foundation Models [PDF]
arXiv.org, 2022 Large pretrained models such as GPT-3 have had tremendous impact on modern natural language processing by leveraging self-supervised learning to learn salient representations that can be used to readily finetune on a wide variety of downstream tasks.
Walid Ahmad +4 more
semanticscholar +1 more source
Augmenting large language models with chemistry tools [PDF]
Nature Machine Intelligence, 2023 Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications.
Andrés M Bran +5 more
semanticscholar +1 more source
Autonomous chemical research with large language models
Nature, 2023 Transformer-based large language models are making significant strides in various fields, such as natural language processing^ 1 – 5 , biology^ 6 , 7 , chemistry^ 8 – 10 and computer programming^ 11 , 12 .
Daniil A. Boiko +3 more
semanticscholar +1 more source
Generative Models as an Emerging Paradigm in the Chemical Sciences
Journal of the American Chemical Society, 2023 Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling.
Dylan M. Anstine, O. Isayev
semanticscholar +1 more source
Neural scaling of deep chemical models
Nature Machine Intelligence, 2023 Massive scale, in terms of both data availability and computation, enables important breakthroughs in key application areas of deep learning such as natural language processing and computer vision.
Nathan C Frey +6 more
semanticscholar +1 more source
Large-scale chemical language representations capture molecular structure and properties [PDF]
Nature Machine Intelligence, 2021 Models based on machine learning can enable accurate and fast molecular property predictions, which is of interest in drug discovery and material design.
Jerret Ross +5 more
semanticscholar +1 more source
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems [PDF]
Chemical Reviews, 2021 Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods.
J. Keith +6 more
semanticscholar +1 more source
Alternatives to laboratory animals : ATLA, 2021 Across multiple sectors, including food, cosmetics and pharmaceutical industries, there is a need to predict the potential effects of xenobiotics. These effects are determined by the intrinsic ability of the substance, or its derivatives, to interact ...
C. Thompson +9 more
semanticscholar +1 more source
Chemprop: A Machine Learning Package for Chemical Property Prediction
Journal of Chemical Information and Modeling, 2023 Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts.
Esther Heid +8 more
semanticscholar +1 more source
The SIR dynamic model of infectious disease transmission and its analogy with chemical kinetics [PDF]
PeerJ Physical Chemistry, 2020 Mathematical models of the dynamics of infectious disease transmission are used to forecast epidemics and assess mitigation strategies. In this article, we highlight the analogy between the dynamics of disease transmission and chemical reaction kinetics ...
Cory M. Simon
doaj +2 more sources