Re-evaluating Retrosynthesis Algorithms with Syntheseus
- URL: http://arxiv.org/abs/2310.19796v2
- Date: Mon, 19 Feb 2024 18:50:09 GMT
- Title: Re-evaluating Retrosynthesis Algorithms with Syntheseus
- Authors: Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang
Xie, Piotr Gai\'nski, Philipp Seidl, Marwin Segler
- Abstract summary: We present a benchmarking library called syntheseus which promotes best practice by default.
We find that the ranking of state-of-the-art models changes when evaluated carefully.
- Score: 14.82497597573977
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The planning of how to synthesize molecules, also known as retrosynthesis,
has been a growing focus of the machine learning and chemistry communities in
recent years. Despite the appearance of steady progress, we argue that
imperfect benchmarks and inconsistent comparisons mask systematic shortcomings
of existing techniques. To remedy this, we present a benchmarking library
called syntheseus which promotes best practice by default, enabling consistent
meaningful evaluation of single-step and multi-step retrosynthesis algorithms.
We use syntheseus to re-evaluate a number of previous retrosynthesis
algorithms, and find that the ranking of state-of-the-art models changes when
evaluated carefully. We end with guidance for future works in this area.
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