Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 162

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Structure‐based Drug Metabolism Predictions for Drug Design

Open access
Sun, Hao, Scott, Dennis O.·2010·Chemical Biology & Drug Design

Abstract

Significant progress has been made in structure‐based drug design by pharmaceutical companies at different stages of drug discovery such as identifying new hits, enhancing molecule binding affinity in hit‐to‐lead, and reducing toxicities in lead optimization. Drug metabolism is a major consideration for modifying drug clearance and also a primary source for drug metabolite‐induced toxicity. With major cytochrome P450 structures identified and characterized recently, structure‐based drug metabolism prediction becomes increasingly attractive.In silico methods based on molecular and quantum mechanics such as docking, molecular dynamics and ab initio chemical reactivity calculations bring us closer to understand drug metabolism and predict drug–drug interactions. In this study, we review important progress in drug metabolism and common in silico techniques adopted to predict drug regioselectivity, stereoselectivity, reactive metabolites, induction, inhibition and mechanism‐based inactivation, as well as their implementation in hit‐to‐lead drug discovery.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/j.1747-0285.2009.00899.x

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

See all 162

Metadata

Authors
Sun, H., & Scott, D. O.
Venue
Chemical Biology & Drug Design
Publisher
Wiley
Published
2009-11-30
Volume
75
Issue
1
Pages
3-17
ISSN
  • 1747-0277 (ISSN-L)
  • 1747-0285
OpenAlex
W1982320452
Cited by
162
References
2
FWCI
16.42
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Structure‐based Drug Metabolism Predictions for Drug Design. https://app.cochat.ai/papers/10.1111/j.1747-0285.2009.00899.x
The paper
Sun, H., & Scott, D. O. (2010). Structure‐based Drug Metabolism Predictions for Drug Design. Chemical Biology & Drug Design. https://doi.org/10.1111/j.1747-0285.2009.00899.x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.