A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction.

Kristy Carpenter, Alexander Pilozzi, Xudong Huang

Abstract read

In one paragraph

Article in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs.Frontiers in oncology · 2022
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kristy CarpenterNeurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA.

Alexander PilozziNeurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA.

Xudong HuangNeurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA.ORCID 0000-0002-4811-1901

Funding

Nanoneurotoxicity of Metal Oxide Nanomaterials and NeurodegenerationR01AG056614 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI HUANG, XUDONG · 2017 to 2020

$1.5M

NIA NIH HHS R01 AG056614NIH HHS R01AG056614

6 · The paper itself

Abstract

The use of virtual drug screening can be beneficial to research teams, enabling them to narrow down potentially useful compounds for further study. A variety of virtual screening methods have been developed, typically with machine learning classifiers at the center of their design. In the present study, we created a virtual screener for protein kinase inhibitors. Experimental compound-target interaction data were obtained from the IDG-DREAM Drug-Kinase Binding Prediction Challenge. These data were converted and fed as inputs into two multi-input recurrent neural networks (RNNs). The first network utilized data encoded in one-hot representation, while the other incorporated embedding layers. The models were developed in Python, and were designed to output the IC

Indexed as

Computer SimulationDeep LearningDrug Evaluation, PreclinicalInhibitory Concentration 50Machine LearningNeural Networks, ComputerPilot ProjectsProtein BindingProtein Kinase InhibitorsProtein KinasesProtein Kinase InhibitorsProtein Kinasesartificial intelligence (AI)deep learning (DL)machine learning (ML)recurrent neural network (RNN)virtual drug screening

Identifiers

PMID32722290

DOI10.3390/molecules25153372

PMCPMC7435591

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.