← Search

Felix Strieth-Kalthoff

4 accepted papers

2026

Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space

ICML 2026poster

Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a prominent example. While surrogate-based optimization can increase sample efficiency by reducing the number of expensive…

Cited by 0SourceScholar
2025

Efficient Evolutionary Search Over Chemical Space with Large Language Models

ICLR 2025poster

Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in molecular discovery, traverse chemical space by perf…

2024

A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?

ICML 2024poster

Automation is one of the cornerstones of contemporary material discovery. Bayesian optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior domain knowledge into efficient exploration of a large molecular space. While such prior knowledge can take many forms, t…

2023

GAUCHE: A Library for Gaussian Processes in Chemistry

NeurIPS 2023poster

We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to molecular repr…