Finding molecules similar to mestranol

Generate molecules with atom-pair similarity to mestranol above 0.75, within 10,000 oracle calls.

shoal State the problem, from PMO (Practical Molecular Optimization) 870f4c7 · 2周前历史

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1	# Finding molecules similar to mestranol
2
3	Rediscovering a known drug from a similarity score is the simplest check that a design algorithm can steer towards a target region of chemical space. Mestranol, a steroid, is the hardest of GuacaMol's three similarity targets, because its fused-ring scaffold is difficult to assemble by small edits.
4
5	## Task
6
7	Propose molecules as SMILES strings and maximise similarity to mestranol, measured as Tanimoto similarity over atom-pair fingerprints and thresholded at 0.75, so anything above the threshold counts as full marks. Scored by AUC top-10 over a 10,000-call budget.
8
9	## Background
10
11	The oracle is one of GuacaMol's similarity benchmarks, served by the Therapeutics Data Commons. In PMO's results it stays much harder than its siblings: albuterol similarity reaches 0.898 while nothing passes 0.63 here. The two are scored with different fingerprints, atom-pair for mestranol against FCFC4 for albuterol, and mestranol's fused steroid rings are harder to assemble by small edits, so the pair is a clean comparison of what current methods can and cannot reach.
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13	## Data and evaluation
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15	- Data: there is no training set to fit. Any database a method needs is restricted to ZINC 250k, which ships
16	with the repository, so no method gets an advantage from a richer corpus.
17	- Budget and seeds: 10,000 oracle calls per run, and five independent runs with different random seeds
18	(`python run.py METHOD --task production --n_runs 5 --oracles mestranol_similarity`). Report the mean and standard deviation
19	over the runs. The budget counts distinct valid canonical SMILES: a molecule queried twice is served from the
20	cache for free, and an invalid SMILES scores 0.
21	- Metric: AUC top-10. The mean score of the ten best molecules found so far is recorded every 100 calls, the
22	curve against the number of calls is integrated by the trapezoid rule and divided by the 10,000-call budget, so
23	the value lands in $[0, 1]$ and higher is better. A run that stops early keeps its final top-10 value for the
24	rest of the budget. Unlike a plain top-10 average, the metric rewards reaching a good score early.
25	- Official evaluation code: <https://github.com/wenhao-gao/mol_opt> (MIT), where `main/optimizer.py` holds the
26	budget accounting and the AUC. Oracles come from the Therapeutics Data Commons via PyTDC (MIT); the ZINC
27	database is free to use.
28
29	## What is known
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31	- Best in the PMO paper's table: GP BO at 0.627 ± 0.089, then REINVENT on SELFIES at 0.620 ± 0.029 and REINVENT at 0.618 ± 0.048. Six methods lie within 0.1 of the leader, so the ranking is not well separated.
32	- Later work on the same protocol reports higher: Mol GA at 0.762 ± 0.048 and Genetic GFN at 0.708 ± 0.057 (five runs, 10,000 calls).
33	- A random screen of ZINC 250k scores 0.409, so roughly two thirds of the leader's score comes for free from the database.
34	- For comparison, the same methods reach about 0.9 on the albuterol similarity oracle, which is what marks this one as the hard case.
35
36	## What would count as progress
37
38	- A mean AUC top-10 above 0.627 on `mestranol_similarity` from five runs under the official protocol, reported
39	with the per-run values and the standard deviation. To beat the field rather than the 2022 table, the number to
40	pass is 0.762 (Mol GA).
41	- The run logs, which record every scored molecule in order, so the AUC and the oracle-call count can be
42	recomputed rather than trusted.
43	- Code in the repository's method layout, so the result can be re-run on the other 22 oracles: a method that wins
44	on one oracle and loses elsewhere is the pattern PMO was built to expose.
45
46	## References
47
48	1. W. Gao, T. Fu, J. Sun and C. W. Coley, "Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization", NeurIPS 2022 Datasets and Benchmarks (2022). <https://arxiv.org/abs/2206.12411>
49	2. N. Brown, M. Fiscato, M. H. S. Segler and A. C. Vaucher, "GuacaMol: Benchmarking Models for de Novo Molecular Design", Journal of Chemical Information and Modeling 59, 1096–1108 (2019). <https://doi.org/10.1021/acs.jcim.8b00839>
50	3. A. Tripp, G. N. C. Simm and J. M. Hernández-Lobato, "A Fresh Look at De Novo Molecular Design Benchmarks", NeurIPS 2021 AI for Science Workshop (2021). <https://openreview.net/forum?id=gS3XMun4cl_>
51	4. A. Tripp and J. M. Hernández-Lobato, "Genetic algorithms are strong baselines for molecule generation" (2023). <https://arxiv.org/abs/2310.09267>
52	5. H. Kim, M. Kim, S. Choi and J. Park, "Genetic-guided GFlowNets for Sample Efficient Molecular Optimization", NeurIPS 2024 (2024). <https://arxiv.org/abs/2402.05961>
53
54	## Source
55
56	Imported from [PMO (mol_opt), the `mestranol_similarity` oracle](https://github.com/wenhao-gao/mol_opt), curated by Wenhao Gao, Tianfan Fu, Jimeng Sun
57	and Connor W. Coley. Open according to the source as of 2026-10-20. The statement above is written in our own
58	words; see the source for the original wording, history and discussion.