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DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra
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DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra
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Mass spectrometry plays a fundamental role in elucidating the structures of unknown molecules and subsequent scientific discoveries. One formulation of the structure elucidation task is the conditional de novo generation of molecular structure given a mass spectrum. Toward a more accurate and efficient scientific discovery pipeline for small molecules, we present DiffMS, a formula-restricted encoder-decoder generative network that achieves state-of-the-art performance on this task. The encoder utilizes a transformer architecture and models mass spectra domain knowledge such as peak formulae and neutral losses, and the decoder is a discrete graph diffusion model restricted by the heavy-atom composition of a known chemical formula. To develop a robust decoder that bridges latent embeddings and molecular structures, we pretrain the diffusion decoder with fingerprint-structure pairs, which are available in virtually infinite quantities, compared to structure-spectrum pairs that number in the tens of thousands. Extensive experiments on established benchmarks show that DiffMS outperforms existing models on de novo molecule generation. We provide several ablations to demonstrate the effectiveness of our diffusion and pretraining approaches and show consistent performance scaling with increasing pretraining dataset size. DiffMS code is publicly available at https://github.com/coleygroup/DiffMS.
Forward citations
Cited by 7 Pith papers
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FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
A diffusion language model over fragment strings, refined by an ICEBERG forward spectral simulator, achieves state-of-the-art de novo molecular identification from tandem mass spectra.
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MS-GPT: Rethinking MS/MS De Novo Structure Elucidation as Spectrum-Induced Posterior Querying of a Molecule-Language Model
Querying a fingerprint-conditioned molecule-language model with a calibrated band of spectrum-induced posteriors beats point-threshold fingerprint decoding on NPLIB1 and MassSpecGym.
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Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning
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CoRe-Gen reaches new state-of-the-art exact-match accuracy on the NPLIB1 benchmark for de novo molecular structure generation from mass spectra by using synthetic pretraining, frequency-aware corruption, and structure...
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FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
FRIGID scales a diffusion-based model for de novo molecular structure generation from mass spectra, reaching over 18% top-1 accuracy on MassSpecGym and tripling prior bests on NPLIB1 via large unlabeled training and i...
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SpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data
SpectraLLM is an LLM fine-tuned to predict small-molecule structures from single or multiple spectra, reporting state-of-the-art results on four public benchmarks with gains from multi-modal input.
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