Dataset Information

Deep Confident Steps to New Pockets: Strategies for Docking Generalization.

ABSTRACT: Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Existing benchmarks, however, fail to rigorously assess generalizability. Therefore, we develop DockGen, a new benchmark based on the ligand-binding domains of proteins, and we show that existing machine learning-based docking models have very weak generalization abilities. We carefully analyze the scaling laws of ML-based docking and show that, by scaling data and model size, as well as integrating synthetic data strategies, we are able to significantly increase the generalization capacity and set new state-of-the-art performance across benchmarks. Further, we propose Confidence Bootstrapping, a new training paradigm that solely relies on the interaction between diffusion and confidence models and exploits the multi-resolution generation process of diffusion models. We demonstrate that Confidence Bootstrapping significantly improves the ability of ML-based docking methods to dock to unseen protein classes, edging closer to accurate and generalizable blind docking methods.

SUBMITTER: Corso G

PROVIDER: S-EPMC10925391 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Deep Confident Steps to New Pockets: Strategies for Docking Generalization.

Corso Gabriele G Deng Arthur A Fry Benjamin B Polizzi Nicholas N Barzilay Regina R Jaakkola Tommi T

ArXiv 20240228

Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Existing benchmarks, however, fail to rigorously assess generalizability. Therefore, we develop DockGen, a new benchmark based on the ligand-binding domains of proteins, and we show that existing machine learning-based docking models have very weak generalization abilities. We carefully analyze the scaling laws of ML-base ...[more]

PMID: 38463508

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Project description:BackgroundIn the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability.PurposeTo develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction.Study typeRetrospective.PopulationEight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites.Field strength/sequenceBrain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T.AssessmentStarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data.Statistical testsMean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model.ResultsOur results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization.Data conclusionWhile further research is needed, GAN-based medical image harmonization appears to be a promising tool for improving cross-site deep learning generalization.Level of evidence4 TECHNICAL EFFICACY: Stage 1.

Dataset Information

Deep Confident Steps to New Pockets: Strategies for Docking Generalization.

Publications

Deep Confident Steps to New Pockets: Strategies for Docking Generalization.

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