Finding molecules similar to mestranol

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

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State the problem, from PMO (Practical Molecular Optimization)

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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.
	12	+
	13	+## Data and evaluation
	14	+
	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
	30	+
	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.