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Found 10 papers, 7 papers with code
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
We present PaperQA, a RAG agent for answering questions over the scientific literature.
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks
Additionally, ease of use remains a concern for any computational technique, resulting in the sustained popularity of group-based contribution methods.
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Recent studies suggested that these models could be useful in chemistry and materials science.
Active Learning in Symbolic Regression with Physical Constraints
Evolutionary symbolic regression (SR) fits a symbolic equation to data, which gives a concise interpretable model.
Censoring chemical data to mitigate dual use risk
The dual use of machine learning applications, where models can be used for both beneficial and malicious purposes, presents a significant challenge.
Bayesian Optimization of Catalysts With In-context Learning
We show a prompting system that enables regression with uncertainty for in-context learning with frozen LLM (GPT-3, GPT-3. 5, and GPT-4) models, allowing predictions without features or architecture tuning.
Recent advances in the Self-Referencing Embedding Strings (SELFIES) library
String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines.
Graph Neural Network Based Coarse-Grained Mapping Prediction
The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation.
Investigating Active Learning and Meta-Learning for Iterative Peptide Design
One way this method can be improved is by ensuring that each experiment provides the best improvement in both peptide properties and predictive modeling accuracy.
Classifying Antimicrobial and Multifunctional Peptides with Bayesian Network Models
Bayesian network models are finding success in characterizing enzyme-catalyzed reactions, slow conformational changes, predicting enzyme inhibition, and genomics.
