Finding isomers of an awkward molecular formula

Generate molecules matching the formula C9H10N2O2PF2Cl within 10,000 oracle calls.

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State the problem, from PMO (Practical Molecular Optimization)

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	1	+# Finding isomers of an awkward molecular formula
	2	+
	3	+Given a molecular formula, can a generative model produce molecules that match it exactly? The formula here mixes phosphorus, fluorine and chlorine with a carbon skeleton, a combination that looks nothing like a typical drug-like molecule, which makes the task a test of flexibility rather than of recall.
	4	+
	5	+## Task
	6	+
	7	+Propose molecules as SMILES strings and maximise the isomer oracle for $\mathrm{C}_9\mathrm{H}_{10}\mathrm{N}_2\mathrm{O}_2\mathrm{PF}_2\mathrm{Cl}$. The score is the geometric mean of Gaussian-modified counts, one for each element in the target formula plus one for the total number of atoms, so every element must be right at once. Scored by AUC top-10 over a 10,000-call budget.
	8	+
	9	+## Background
	10	+
	11	+The isomer benchmarks are GuacaMol's fully-determined tasks: the answer set can in principle be enumerated, and they have no direct drug-discovery use, but they expose whether a model can follow a simple pattern it has not seen. In PMO's results the landscape favours string-level search: genetic algorithms over SMILES and SELFIES lead it, while synthesis- and fragment-based methods score far lower. The paper's own explanation is that isomer oracles sum atomic contributions, unlike the fingerprint-similarity objectives that make up most of the benchmark, so a different family of methods wins.
	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 isomers_c9h10n2o2pf2cl`). 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: SMILES GA at 0.860 ± 0.065, then STONED at 0.805 ± 0.031 and REINVENT on SELFIES at 0.733 ± 0.029.
	32	+- Synthesis-based methods fare badly: SynNet scores 0.241 ± 0.064 and DoG-Gen 0.199 ± 0.016, less than a third of the leader.
	33	+- Later work on the same protocol reports higher: Genetic GFN at 0.897 ± 0.007 and Mol GA at 0.894 ± 0.005 (five runs, 10,000 calls).
	34	+- A random screen of ZINC 250k scores 0.106.
	35	+
	36	+## What would count as progress
	37	+
	38	+- A mean AUC top-10 above 0.86 on `isomers_c9h10n2o2pf2cl` 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 0.897 (Genetic GFN).
	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. W. Gao, R. Mercado and C. W. Coley, "Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design", ICLR 2022 (2021). <https://arxiv.org/abs/2110.06389>
	51	+4. 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>
	52	+5. A. Tripp and J. M. Hernández-Lobato, "Genetic algorithms are strong baselines for molecule generation" (2023). <https://arxiv.org/abs/2310.09267>
	53	+
	54	+## Source
	55	+
	56	+Imported from [PMO (mol_opt), the `isomers_c9h10n2o2pf2cl` 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.