about
Predicting new molecular targets for known drugsIn silico molecular comparisons of C. elegans and mammalian pharmacology identify distinct targets that regulate feedingComplementarity Between a Docking and a High-Throughput Screen in Discovering New Cruzain InhibitorsA mapping of drug space from the viewpoint of small molecule metabolismLeveraging Large-scale Behavioral Profiling in Zebrafish to Explore Neuroactive Polypharmacology.Zebrafish behavioral profiling identifies multitarget antipsychotic-like compounds.The presynaptic component of the serotonergic system is required for clozapine's efficacy.Quantifying biogenic bias in screening libraries.Prediction and evaluation of protein farnesyltransferase inhibition by commercial drugs.Systems pharmacology augments drug safety surveillanceChemical informatics and target identification in a zebrafish phenotypic screen.Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach.A pilot study of the pharmacodynamic impact of SSRI drug selection and beta-1 receptor genotype (ADRB1) on cardiac vital signs in depressed patients: a novel pharmacogenetic approach.Quantifying the relationships among drug classesPrediction and validation of enzyme and transporter off-targets for metformin.A Simple Representation of Three-Dimensional Molecular Structure.Evolutionarily Conserved Roles for Blood-Brain Barrier Xenobiotic Transporters in Endogenous Steroid Partitioning and Behavior.Predicted Biological Activity of Purchasable Chemical Space.Adversarial Controls for Scientific Machine LearningThe Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric DisordersInterpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipelineZebrafish behavioural profiling identifies GABA and serotonin receptor ligands related to sedation and paradoxical excitationComment on "Predicting reaction performance in C-N cross-coupling using machine learning"Validation of machine learning models to detect amyloid pathologies across institutionsLearning Molecular Representations for Medicinal Chemistry
P50
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P50
description
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Michael J Keiser
@ast
Michael J Keiser
@en
Michael J Keiser
@es
Michael J Keiser
@nl
type
label
Michael J Keiser
@ast
Michael J Keiser
@en
Michael J Keiser
@es
Michael J Keiser
@nl
prefLabel
Michael J Keiser
@ast
Michael J Keiser
@en
Michael J Keiser
@es
Michael J Keiser
@nl
P106
P31
P496
0000-0002-1240-2192