• Corpus ID: 231855503

Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19

@article{Ward2021BenchmarkingDG,
  title={Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19},
  author={Logan T. Ward and Jenna A. Bilbrey and Sutanay Choudhury and Neeraj Kumar and Ganesh Sivaraman},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.04977}
}
Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative models and a comparison study of two state-of-the-art graph generative models for designing COVID-19 targeted drug candidates: 1) a variational autoencoder-based approach (VAE) that uses prior knowledge of molecules that have been shown to be effective for earlier coronavirus treatments and 2) a deep Q-learning method… 

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References

SHOWING 1-10 OF 50 REFERENCES
Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
TLDR
A deep learning based generative modeling framework to design drug candidates specific to a given target protein sequence with high off-target selectivity is presented, and an in silico screening process that accounts for toxicity is augmented to lower the failure rate of the generated drug candidates in later stages of the drug development pipeline.
PaccMannRL on SARS-CoV-2: Designing antiviral candidates with conditional generative models
TLDR
A deep learning framework for conditional de novo design of antiviral candidate drugs tailored against given protein targets is proposed and a framework that navigates the chemical space toward regions with more antiviral molecules is showcased.
Multi-objective de novo drug design with conditional graph generative model
TLDR
A new de novo molecular design framework is proposed based on a type of sequential graph generators that do not use atom level recurrent units, which is much more tuned for molecule generation and has been scaled up to cover significantly larger molecules in the ChEMBL database.
Multi-Objective Molecule Generation using Interpretable Substructures
TLDR
This work proposes to offset the complexity of the generative modeling of molecules by composing molecules from a vocabulary of substructures that are likely responsible for each property of interest, called molecular rationales.
Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design
TLDR
A fragment-based reinforcement learning approach based on an actor-critic model for the generation of novel molecules with optimal properties for medicinal chemistry programs, demonstrating that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives.
DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach
TLDR
The method is extended to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine, and should be generally applicable to the generation in silico of molecules with desirable properties.
SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines
TLDR
A method is presented called SimBoost that predicts continuous (non-binary) values of binding affinities of compounds and proteins and thus incorporates the whole interaction spectrum from true negative to true positive interactions and outperform the previously reported models across the studied datasets.
Screening of Therapeutic Agents for COVID-19 using Machine Learning and Ensemble Docking Simulations
TLDR
This work uses a powerful and efficient computational strategy by combining machine learning (ML) based models and high-fidelity ensemble docking simulations to enable rapid screening of possible therapeutic molecules (or ligands) against COVID-19.
DeepPurpose: a Deep Learning Based Drug Repurposing Toolkit
TLDR
With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug/target encodings and 50+ novel architectures.
Optimization of Molecules via Deep Reinforcement Learning
TLDR
Inspired by problems faced during medicinal chemistry lead optimization, the MolDQN model is extended with multi-objective reinforcement learning, which maximizes drug-likeness while maintaining similarity to the original molecule.
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