Reproduce “Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms” on OpenResearch
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About this paper
Samuel Stanton, Robert Alberstein, Nathan Frey, Andrew Watkins, Kyunghyun Cho
Prescient Design at Genentech introduces "Ehrlich functions," a new class of closed-form, synthetic test functions designed for benchmarking biophysical sequence optimization algorithms. These functions provide a computationally efficient and tunable means to evaluate algorithms, addressing the scarcity of fast and reproducible benchmarks for machine learning in drug discovery and protein engineering.
Key insights
- Ehrlich functions allow for precise control over problem difficulty through parameters such as sequence length, motif count, and especially objective quantization (q).
- These functions can be used to systematically analyze and understand optimizer behavior, revealing critical trade-offs like balancing exploration depth with maintaining population feasibility.
- The benchmarks provide rapid feedback, allowing for the reproduction of complex results requiring hundreds of millions of function evaluations within minutes on a single GPU.
Summary by alphaXiv.
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