Evidence map›Paper›PMID 38706556›Full record

ArticleACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine2023

Physics-Guided Deep Generative Model for New Ligand Discovery.

Dikshant Sagar, Ali Risheh, Nida Sheikh, Negin Forouzesh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2023. 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.

  1. 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

4 authors.

Dikshant SagarDepartment of Computer Science, California State University, Los Angeles, Los Angeles, California, USA.
Ali RishehDepartment of Computer Science, California State University, Los Angeles, Los Angeles, California, USA.
Nida SheikhDepartment of Computer Science, California State University, Los Angeles Los Angeles, California, USA.
Negin ForouzeshDepartment of Computer Science, California State University, Los Angeles, Los Angeles, California, USA.

Funding

Improving the Accuracy of Implicit Solvents with a Physics-Guided Neural NetworkR16GM146633 · NIGMS · CALIFORNIA STATE UNIVERSITY LOS ANGELES · PI FOROUZESH, NEGIN · 2022 to 2025
$755k
NIGMS NIH HHS R16 GM146633
6 · The paper itself

Abstract

Structure-based drug discovery aims to identify small molecules that can attach to a specific target protein and change its functionality. Recently, deep learning has shown great promise in generating drug-like molecules with specific biochemical features and conditioned with structural features. However, they usually fail to incorporate an essential factor: the underlying physics which guides molecular formation and binding in real-world scenarios. In this work, we describe a physics-guided deep generative model for new ligand discovery, conditioned not only on the binding site but also on physics-based features that describe the binding mechanism between a receptor and a ligand. The proposed hybrid model has been tested on large protein-ligand complexes and small host-guest systems. Using the top-

Indexed as

Deep learningDrug discoveryGenerative neural networksImplicit solvent models

Identifiers

PMID38706556
PMCPMC11067829

What OpenQuestion holds

Textmetadata
LicenceTDM
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Registered trials

None linked

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.