Evidence map›Paper›PMID 37675804›Full record

ArticleJournal of medicinal chemistry2023

Molecular Complexity: You Know It When You See It.

Tudor I Oprea, Cristian Bologa

Abstract read
In one paragraph

Article in Journal of medicinal chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. A View on Molecular Complexity from the GDB Chemical Space.Journal of chemical information and modeling · 2025
    Review
  6. Article
  7. Review
  8. Latin American Natural Product Database (LANaPDB): An Update.Journal of chemical information and modeling · 2024
    Article
  9. 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

2 authors.

Tudor I OpreaExpert Systems Inc, 12760 High Bluff Dr #370, San Diego, California 92130, United States.ORCID 0000-0002-6195-6976
Cristian BologaDepartment of Internal Medicine, University of New Mexico, MSC09-5025, Albuquerque, New Mexico 87131, United States.

Funding

Machine Learning Model Validation for AD/ADRDU24CA224370 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI EDWARDS, JEREMY S · 2018 to 2023
$6.4M
NCI NIH HHS U24 CA224370
6 · The paper itself

Abstract

Molecular complexity (MC) lacks a universal definition, but various studies address it in contexts ranging from ligand-receptor interactions to DNA sequencing, with the overarching emphasis being its significance in synthetic organic chemistry and pharmaceutical research. Efforts to quantify MC in drug discovery have been numerous, but a unified approach remains challenging. Strategies based on graph theory, information theory, and substructural feature counts employed to gauge MC are often correlated to molecular weight (MW). Herbert Waldmann and his team introduced a new MC metric called the spacial score (SPS), which is based on factors like atom hybridization and stereoisomeric considerations. While SPS and its normalized version, nSPS, correlate with the natural product likeness score, they do not align with traditional chemical properties. We examined nSPS trends for approved drugs and found no significant changes in MC over eight decades, nor did nSPS capture drug innovation during that period. Furthermore, our analysis indicates that while the majority of approved drugs have an nSPS value between 10 and 20, this metric does not correlate with key drug properties like target bioactivity and oral bioavailability. Mirroring a chemist's intuitive sense of chemical complexity, nSPS addresses the need for a precise empirical tool while a universal definition of MC remains elusive.

Indexed as

Biological ProductsDrug DiscoveryMolecular WeightBiological Products

Identifiers

PMID37675804
PMCPMC10544322

What OpenQuestion holds

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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.