Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Applied machine learning as a driver for polymeric biomaterials design
Polymers are ubiquitous to almost every aspect of modern society and their use in medical
products is similarly pervasive. Despite this, the diversity in commercial polymers used in …
products is similarly pervasive. Despite this, the diversity in commercial polymers used in …
Chemformer: a pre-trained transformer for computational chemistry
Transformer models coupled with a simplified molecular line entry system (SMILES) have
recently proven to be a powerful combination for solving challenges in cheminformatics …
recently proven to be a powerful combination for solving challenges in cheminformatics …
Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
The field of predictive chemistry relates to the development of models able to describe how
molecules interact and react. It encompasses the long-standing task of computer-aided …
molecules interact and react. It encompasses the long-standing task of computer-aided …
Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review
Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This
development has been further accelerated with the increasing use of machine learning (ML) …
development has been further accelerated with the increasing use of machine learning (ML) …
Molecule edit graph attention network: modeling chemical reactions as sequences of graph edits
M Sacha, M Błaz, P Byrski… - Journal of Chemical …, 2021 - ACS Publications
The central challenge in automated synthesis planning is to be able to generate and predict
outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely …
outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely …
An ensemble transfer learning strategy for production prediction of shale gas wells
W Niu, Y Sun, X Zhang, J Lu, H Liu, Q Li, Y Mu - Energy, 2023 - Elsevier
In order to overcome the training data insufficient problem of model for shale gas wells
production prediction in new block, this study proposes a transfer learning strategy of …
production prediction in new block, this study proposes a transfer learning strategy of …
Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions
Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity
produced by the contraction and relaxation of the cardiac muscles. It has been established …
produced by the contraction and relaxation of the cardiac muscles. It has been established …
Artificial intelligence in reaction prediction and chemical synthesis
Recent years have seen a sudden spurt in the use of artificial intelligence (AI) methods for
computational reaction modeling and prediction. Given the diversity of the techniques, we …
computational reaction modeling and prediction. Given the diversity of the techniques, we …
[HTML][HTML] User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals
Artificial intelligence has brought crucial changes to the whole field of natural sciences.
Myriads of machine learning algorithms have been developed to facilitate the work of …
Myriads of machine learning algorithms have been developed to facilitate the work of …