Properties of molecules are indicative of their functions and thus are useful in many applications. As a cost-effective alternative to experimental approaches, computational methods for predicting molecular properties are gaining increasing momentum and success . However, there lacks a comprehensive collection of tools and methods for this task currently. Here we develop the MoleculeKit, a suite of comprehensive machine learning  tools spanning different computational models and molecular representations for molecular property prediction and drug discovery. Specifically, MoleculeKit represents molecules as both graphs and sequences. Built on these representations, MoleculeKit includes both deep learning and traditional machine learning methods for graph and sequence data. Noticeably, we propose and develop novel deep models for learning from molecular graphs and sequences. Therefore, MoleculeKit not only serves as a comprehensive tool, but also contributes towards developing novel and advanced graph and sequence learning methodologies. Results on both online and offline antibiotics discovery and molecular property prediction tasks show that MoleculeKit achieves consistent improvements over prior methods  .
Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep learning methods, computational approaches for predicting molecular properties are gaining increasing momentum . However, there lacks customized and advanced methods and comprehensive tools for this task currently. Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models , molecular representations, and loss functions for molecular property prediction and drug discovery. Specifically, we represent molecules as both graphs and sequences. Built on these representations, we develop novel deep models for learning from molecular graphs and sequences. In order to learn effectively from highly imbalanced datasets, we develop advanced loss functions that optimize areas under precision-recall curves. Altogether, our work not only serves as a comprehensive tool, but also contributes towards developing novel and advanced graph and sequence learning methodologies. Results on both online and offline antibiotics discovery and molecular property prediction tasks show that our methods achieve consistent improvements over prior methods . In particular, our methods achieve #1 ranking in terms of both ROC-AUC and PRC-AUC on the AI Cures Open Challenge for drug discovery related to COVID-19. Our software is released as part of the MoleculeX library under AdvProp .
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As a cost-effective alternative to experimental approaches, computational methods
With the advances of deep learning methods, computational approaches
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and success
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a comprehensive collection of tools and methods
customized and advanced methods and comprehensive tools
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the MoleculeKit,
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methods and
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and molecular representations
, molecular representations, and loss functions
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MoleculeKit represents
we represent
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MoleculeKit includes both deep learning and traditional machine learning methods for graph and sequence data. Noticeably, we propose and
we
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Therefore, MoleculeKit
In order to learn effectively from highly imbalanced datasets, we develop advanced loss functions that optimize areas under precision-recall curves. Altogether, our work
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MoleculeKit achieves
our methods achieve
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. In particular, our methods achieve #1 ranking in terms of both ROC-AUC and PRC-AUC on the AI Cures Open Challenge for drug discovery related to COVID-19. Our software is released as part of the MoleculeX library under AdvProp