Known Molecule
Research on known molecules focuses on developing efficient methods for generating, optimizing, and predicting their properties, primarily to accelerate drug discovery and materials science. Current efforts leverage machine learning, employing architectures like graph neural networks, transformers, and diffusion models, often incorporating 3D structural information and multi-fidelity approaches to improve accuracy and efficiency. These advancements enable more rapid exploration of chemical space, leading to improved predictions of molecular properties and the design of molecules with desired characteristics, ultimately impacting drug development and materials design.
112papers
Papers - Page 4
February 2, 2024
December 27, 2023
December 26, 2023
December 6, 2023
November 27, 2023
November 26, 2023
November 19, 2023
November 6, 2023
October 25, 2023
Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds
Jaiveer Gill, Ratul Chakraborty, Reetham Gubba, Amy Liu, Shrey Jain, Chirag Iyer, Obaid Khwaja, Saurav KumarFrom Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Nima Shoghi, Adeesh Kolluru, John R. Kitchin, Zachary W. Ulissi, C. Lawrence Zitnick, Brandon M. Wood