Robust deep learning based protein sequence design using ProteinMPNN

1 code implementation • bioRxiv 2022 • J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker

While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta.

Drug Discovery Protein Design +3

Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures

no code implementations • 21 Oct 2020 • H. Bai, L. Tan, K. Xiong, M. Li, J. Lin

In this paper, we demonstrate under a Bayesian framework that distance between primitive statistics such as the mean of word embeddings are fundamentally flawed for capturing sentence-level semantic similarity.

Semantic Similarity Semantic Textual Similarity +3

Compressed Sensing with Probability-based Prior Information

no code implementations • 27 Oct 2019 • Q. Jiang, S. Li, Z. Zhu, H. Bai, X. He, R. C. de Lamare

This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing system.