Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Abstract Design of de novo biological sequences with desired properties, like protein and
DNA sequences, often involves an active loop with several rounds of molecule ideation and …

Generative Flow Networks (GFlowNets) have been introduced as a method to sample a
diverse set of candidates in an active learning context, with a training objective that makes …

This paper is about the problem of learning a stochastic policy for generating an object (like
a molecular graph) from a sequence of actions, such that the probability of generating an …

Tackling the most pressing problems for humanity, such as the climate crisis and the threat
of global pandemics, requires accelerating the pace of scientific discovery. While science …

Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …

Antibodies are multimeric proteins capable of highly specific molecular recognition. The
complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often …

This article develops a Bayesian optimization (BO) method which acts directly over raw
strings, proposing the first uses of string kernels and genetic algorithms within BO loops …

We consider the problem of optimizing combinatorial spaces (eg, sequences, trees, and
graphs) using expensive black-box function evaluations. For example, optimizing molecules …

Designing protein sequences with desired biological function is crucial in biology and
chemistry. Recent machine learning methods use a surrogate sequence-function model to …