2021/10/06 by Dinghuai Zhang, Zhang, Dinghuai, Jie Fu +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Biomolecules (q-bio.BM) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #cs.AI #cs.LG #q-bio.BM #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.2110.03372
ICLR 2022 spotlight
openalex publication_date 2021/10/06 · arxiv created 2022/02/08 · arxiv updated 2022/02/10 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry. In this work, we propose to unify two seemingly distinct worlds: likelihood-free inference and black-box optimization, under one probabilistic framework. In tandem, we provide a recipe for constructing various sequence design methods based on this framework. We show how previous optimization approaches can be "reinvented" in our framework, and further propose new probabilistic black-box optimization algorithms. Extensive experiments on sequence design application illustrate the benefits of the proposed methodology.