Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery

Preuer, Kristina; Renz, Philipp; Unterthiner, Thomas; Hochreiter, Sepp; Klambauer, Günter

doi:10.1021/acs.jcim.8b00234

Cited by 274 publications

(239 citation statements)

References 25 publications

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“…where " is the chemical language model prediction for token , is the temperature, and " is the sampling probability of token given by the chemical language model. (https://github.com/bioinf-jku/FCD) 27 . In total, 5000 molecules were randomly selected from each compound set for FCD calculation when possible.…”

Section: Methodsmentioning

confidence: 99%

“…To estimate the coverage of the chemical space during transfer learning, we computed the Fréchet ChemNet Distance (FCD), a distance metric to evaluate the similarity between two populations of molecules based on chemical structure and bioactivity 27 . An FCD value of 0 indicates that the compared molecular spaces are identical, while higher values indicate greater dissimilarity.…”

Section: Generating Application-focused Compound Librariesmentioning

confidence: 99%

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Generating Customized Compound Libraries for Drug Discovery with Machine Intelligence

Moret¹,

Friedrich²,

Grisoni³

et al. 2019

Preprint

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Generative machine learning models sample drug-like molecules from chemical space without the need for explicit design rules. A deep learning framework for customized compound library generation is presented, aiming to enrich and expand the pharmacologically relevant chemical space with new molecular entities 'on demand'. This de novo design approach was used to generate molecules that combine features from bioactive synthetic compounds and natural products, which are a primary source of inspiration for drug discovery. The results show that the data-driven machine intelligence acquires implicit chemical knowledge and generates novel molecules with bespoke properties and structural diversity. The method is available as an open-access tool for medicinal and bioorganic chemistry.Innovative molecular design methods are needed to support medicinal chemistry by efficient sampling of untapped drug-like chemical space 1,2,3 . Recently, the field of drug design has adopted so-called generative deep learning models to construct new molecules with desired properties 4,5,6,7,8 . Deep learning methods represent a class of machine learning algorithms that learn directly from the input data and do not necessarily depend on rules coded by humans 9,10 . Some of these methods implement a language modeling approach 11 , where an artificial neural network aims to learn the probability of a 'token' (e.g., a word or a character) to appear in a sequence based on the distributions all previous tokens in a sequence 12 . Through this process, deep neural networks can learn the features of sequential data. Once trained, these models can generate novel sequences based on the sampled feature distributions.

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Section: Methodsmentioning

confidence: 99%

Section: Generating Application-focused Compound Librariesmentioning

confidence: 99%

Generating Customized Compound Libraries for Drug Discovery with Machine Intelligence

Moret¹,

Friedrich²,

Grisoni³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Maximum mean discrepancy (MMD) is one of such metrics that have been widely applied in generative modeling (both for model training 34 and evaluation 35 ). Different from other metrics, such as Jensen-Shannon divergence (JSD), Wasserstein distance (WD) 36 or Frechet ChemNet distance (FCD), 37 MMD does not require additional discriminator or preditor to be evaluated, which makes it much easier to calculate.…”

Section: Chemical Validity and Uniquenessmentioning

confidence: 99%

DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning

Wang

et al. 2019

J. Chem. Inf. Model.

112

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The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as the core structures of the molecules is one of the efficient ways to obtain potential drug candidates with desirable properties. We proposed a scaffold-based molecular generative model for scaffold-based drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including BM-scaffolds, cyclic skeletons, as well as scaffolds with specifications on side-chain properties. The model can generalize the learned chemical rules of adding atoms and bonds to a given scaffold. Furthermore, the generated compounds were evaluated by molecular docking in DRD2 targets and the results demonstrated that this approach can be effectively applied to solve several drug arXiv:1908.07209v4 [q-bio.QM] 5 Sep 2019 design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores.

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“…"ChemAI" is a deep neural network trained to simultaneously predict a large number of biological effects (Mayr et al, 2018;Preuer et al, 2019). In more detail, the network is of the type SmilesLSTM (Mayr et al, 2018;Hochreiter & Schmidhuber, 1997) and trained on a data set comprised of ChEMBL (Gaulton et al, 2017), ZINC (Sterling & Irwin, 2015) and PubChem (Kim et al, 2016), and which is similar to the data set used by Preuer et al (2018). ChemAI predicts 6,269 biological outcomes, such as binding to targets, inhibitory or toxic effects.…”

Section: Technical Reportmentioning

confidence: 99%

Large-Scale Ligand-Based Virtual Screening for SARS-CoV-2 Inhibitors Using Deep Neural Networks

et al. 2020

Self Cite

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Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI," a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and Drug-Bank as a library for further screening with bioassays at https://github.com/ml-jku/ sars-cov-inhibitors-chemai.

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Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery

Cited by 274 publications

References 25 publications

Generating Customized Compound Libraries for Drug Discovery with Machine Intelligence

Generating Customized Compound Libraries for Drug Discovery with Machine Intelligence

DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning

Large-Scale Ligand-Based Virtual Screening for SARS-CoV-2 Inhibitors Using Deep Neural Networks

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