Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

@article{Segler2017GeneratingFM,
  title={Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks},
  author={Marwin H. S. Segler and Thierry Kogej and Christian Tyrchan and Mark P. Waller},
  journal={ACS Central Science},
  year={2017},
  volume={4},
  pages={120 - 131}
}
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. [] Key Result When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.

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References

SHOWING 1-10 OF 75 REFERENCES

Chemical-Space-Based de Novo Design Method To Generate Drug-Like Molecules

The modified DAECS is modified to enable the user to select a target area to consider properties other than activity and improve the diversity of the generated structures by visualizing the drug-likeness distribution and the activity distribution.

Multi-objective molecular de novo design by adaptive fragment prioritization.

The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand-based computer-aided molecular design method may guide target-focused combinatorial chemistry.

Development of a New De Novo Design Algorithm for Exploring Chemical Space

A new de novo design system to search a target area in chemical space and showed that the proposed generator could produce diverse virtual compounds that had high activity in docking simulations.

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

A deterministic compound construction procedure that explicitly considers compound synthesizability is implemented, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles, and proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.

Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules

A brief overview of deep learning methods is presented and in particular how recursive neural network approaches can be applied to the problem of predicting molecular properties, by considering an ensemble of recursive neural networks associated with all possible vertex-centered acyclic orientations of the molecular graph.

Enabling future drug discovery by de novo design

An overview of the various methodologies for virtual compound construction, evaluation, and optimization in machina, and how they can support medicinal chemistry projects in the early phase of drug discovery is given.

Low Data Drug Discovery with One-Shot Learning

This work demonstrates how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications and introduces a new architecture, the iterative refinement long short-term memory, that significantly improves learning of meaningful distance metrics over small-molecules.

Deep Learning in Drug Discovery

An overview of this emerging field of molecular informatics, the basic concepts of prominent deep learning methods are presented, and motivation to explore these techniques for their usefulness in computer‐assisted drug discovery and design is offered.

SCUBIDOO: A Large yet Screenable and Easily Searchable Database of Computationally Created Chemical Compounds Optimized toward High Likelihood of Synthetic Tractability

SCUBIDOO is a freely accessible database concept that currently holds 21 million virtual products originating from a small library of building blocks and a collection of robust organic reactions, and might be a useful idea generator for early ligand discovery projects since it allows a focus on those molecules that are likely to be synthetically feasible and can therefore be studied further.

The enumeration of chemical space

The chemical space of molecules following simple rules of chemical stability and synthetic feasibility has been enumerated up to 13 atoms of C, N, O, Cl, S, forming the GDB‐13 database with 977 million structures, which is organized in a 42‐dimensional chemical space using molecular quantum numbers (MQN) as descriptors.
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