Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning

no code implementations • 10 Aug 2022 • Siba Moussa, Michael Kilgour, Clara Jans, Alex Hernandez-Garcia, Miroslava Cuperlovic-Culf, Yoshua Bengio, Lena Simine

Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria.

Biological Sequence Design with GFlowNets

1 code implementation • 2 Mar 2022 • Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio

In this work, we propose an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful (as defined by some utility function, for example, the predicted anti-microbial activity of a peptide) and informative candidates after each round.

Active Learning