Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

Lenselink, Eelke B.; Dijke, Niels ten; Bongers, Brandon J.; Papadatos, George; Vlijmen, Herman van; Kowalczyk, Wojtek; IJzerman, Adriaan P.; Westen, Gerard J. P. van

doi:10.1186/s13321-017-0232-0

Cited by 256 publications

(259 citation statements)

References 41 publications

(52 reference statements)

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“…[206] Current achievements in reaction prediction and retrosynthesis demonstrate the potential of machine learning to solve one of the bottlenecks of state-of-the-art materials design, which is the planning of efficient reaction routes of new molecules. Examples can be given for, e.g., feature detection, [207] bioactivity prediction, [208] or drug target prediction, [209] and others. Examples can be given for, e.g., feature detection, [207] bioactivity prediction, [208] or drug target prediction, [209] and others.…”

Section: Discussionmentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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organic/inorganic perovskites solar cells [6] to printable electronic circuits based on organic field-effect transistors (OFETs). [7] While OLED displays outperform their inorganic counterparts in terms of energy efficiency, [8] scientific and technical challenges concerning the stability and processability of the organic materials used in large-area OLEDs and organic solar cells remain. New challenges arise from applications, such as displays on flexible substrates, OLED lightning, large area displays as well as for printable or solution processable larger area solar cells. [8] Many of the remaining challenges are material related, e.g., the low mobility of charge carriers in organic materials in general and in amorphous organic semiconductors in particular. There are other materials related issues, such as limited OLED life times due to unstable blue hosts and emitters, [9] low fill factors, and therefore reduced power conversion efficiencies of organic solar cells, [10,11] low conductivity and high costs of organic charge transport layers of perovskite solar cells [6,12,13] and low conductivity and hard processability of crystalline OFET materials. [14] Conductivity and injection can be improved by doping the organic thin films with molecular dopants with high electron affinities (p-type) [15] or low ionization energies (n-type). [16] The doping mechanism of organic materials is in many cases not well understood, [17] making material and device optimization a costly experimental endeavor.The development of better materials is presently based on chemical insight, in part guided by theoretical understanding, or the experimental screening of large numbers of compounds. Given the size of the potentially available chemical space this remains a costly and time-consuming approach. Recent successes in experimental design of novel materials and concepts include the development of a stable strong molecular n-type dopant, [16] a study about the quantitative relation between interaction parameter, miscibility, and function of conjugated polymer donors and small-molecule acceptors for bulk heterojunctions as used in organic solar cells [18] the development of a universal strategy for ohmic hole injection into organic semiconductors with high ionization energies [19] and many others. [20][21][22][23][24][25] Another example is the development of strategies to harvest triplet excitons in organic light emitting diodes. For this purpose, novel classes of emitter molecules were developed, which include thermally activated delayed fluorescence (TADF)-based molecules, [26][27][28][29][30][31][32] rotationally accessed spin-state inversion, [33] and radical-based emitters. [34] Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large-area devices, and thin-film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly inf...

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Section: Discussionmentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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“…Although there are several chemistry problems where DNNs outperform other shallow machine learning methods 49,59,60 , here the MFP+RF performed best with the small dataset of 676 molecules in the 5-and 12-class predictions. However, in the 3class task with the small dataset, and all the tasks with the large dataset, the two models produced accuracies that were nearly indistinguishable.…”

Section: Discussionmentioning

confidence: 85%

Learning Drug Function from Chemical Structure with Convolutional Neural Networks and Random Forests

Meyer

Liu

Miller

et al. 2018

Preprint

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Empirical testing of chemicals for drug efficacy costs many billions of dollars every year. The ability to predict the action of molecules in silico would greatly increase the speed and decrease the cost of prioritizing drug leads. Here, we asked whether drug function, defined as MeSH "Therapeutic Use" classes, can be predicted from only chemical structure. We evaluated two chemical structure-derived drug classification methods, chemical images with convolutional neural networks and molecular fingerprints with random forests, both of which outperformed previous predictions that used druginduced transcriptomic changes as chemical representations. This suggests that a chemical's structure contains at least as much information about its therapeutic use as the transcriptional cellular response to that chemical. Further, because training data based on chemical structure is not limited to a small set of molecules for which transcriptomic measurements are available, our strategy can leverage more training data to significantly improve predictive accuracy to 83-88%. Finally, we explore use of these models for prediction of side effects and drug repurposing opportunities, and demonstrate the effectiveness of this modeling strategy for multi-label classification.

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“…For tankyrase, due to the relatively small number of known inhibitors, the improvement from 0.81 to 0.93 (random test set split) or 0.89 (time‐based split) was obtained. As can be seen, the Deep Neural Networks (DNN) generally yield the best performing models, consistent with the results obtained previously . Using the network with two hidden layers (300 and 100 neurons), the external test AUC ROC values of 0.96 and 0.93 are obtained for the random split and 0.90 and 0.84 for the time‐based split of the PI3Kα and tankyrase inhibitors, respectively.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Classification Models to Improve the Docking‐based Screening: A Case of PI3K‐Tankyrase Inhibitors

Berishvili

Voronkov

Radchenko

et al. 2018

Molecular Informatics

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One of the major challenges in the current drug discovery is the improvement of the docking-based virtual screening performance. It is especially important in the rational design of compounds with desired polypharmacology or selectivity profiles. To address this problem, we present a methodology for the development of target-specific scoring functions possessing high screening power. These scoring functions were built using the machine learning methods for the dual target inhibitors of PI3Kα and tankyrase, promising targets for colorectal cancer therapy. The Deep Neural Network models achieve the external test AUC ROC values of 0.96 and 0.93 for the random split and 0.90 and 0.84 for the time-based split of the PI3Kα and tankyrase inhibitors, respectively. In addition, the impact of the training set size and the actives/decoys ratio on the model quality was assessed. The study demonstrates that the optimized scoring functions could significantly improve the docking screening power for each individual target. This is very useful in the design of multitarget or selective drugs wherein the screening filters are applied in sequence.

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Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

Cited by 256 publications

References 41 publications

Toward Design of Novel Materials for Organic Electronics

Toward Design of Novel Materials for Organic Electronics

Learning Drug Function from Chemical Structure with Convolutional Neural Networks and Random Forests

Machine Learning Classification Models to Improve the Docking‐based Screening: A Case of PI3K‐Tankyrase Inhibitors

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