Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening

Paricharak, Shardul; Méndez‐Lucio, Oscar; Bender, Andreas; IJzerman, Adriaan P.

doi:10.1093/bib/bbw105

Cited by 22 publications

(25 citation statements)

References 97 publications

(138 reference statements)

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“…One alternative is to use computational methods and data-driven approaches to aid hit identification 4,5 . Techniques such as virtual screening aim to identify hits from virtual libraries containing large number of molecules, usually by similarity-based searches or by molecular docking 4,6,7 .…”

Section: Introductionmentioning

confidence: 99%

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

et al. 2020

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Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.

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Section: Introductionmentioning

confidence: 99%

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

et al. 2020

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“…However, the cellular response to a compound can be described without taking its chemical structure into account. Instead, the so-called bioactivity profile can be used to quantitatively describe compound interactions with the proteome [4,5]. It was demonstrated that the comparison of compounds by their bioactivity profiles rather than by their structures can lead to the discovery of structurally dissimilar compounds eliciting same biological responses [6].…”

Section: Introductionmentioning

confidence: 99%

QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping

et al. 2020

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An affinity fingerprint is the vector consisting of compound's affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Mor-gan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.

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“…The importance of selecting various chemical compounds in the initial screening has been consistently reported 49 , 50 . The greater the structural diversity of a training set is and the more scaffolds there are, the larger the applicability domain of the model 6 .…”

Section: Resultsmentioning

confidence: 99%

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks

Sakai

Nagayasu

Shibui

et al. 2021

Sci Rep

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Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies.

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Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening

Cited by 22 publications

References 97 publications

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks

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