ArticleACS omega2022
Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review.European journal of drug metabolism and pharmacokinetics · 2024Pooled it
- Activity-cliff awareness enables robust graph learning for molecular property prediction.Nature communications · 2026Article
- Test-Time Training Scaling Laws for Chemical Exploration in Drug Design.Journal of chemical information and modeling · 2025Article
- Development and Validation of Atomic Group Descriptors for Substituent Effects.Journal of computational chemistry · 2025Article
- From mundane to surprising nonadditivity: drivers and impact on ML models.Journal of computer-aided molecular design · 2024Article
- Bidirectional generation of structure and properties through a single molecular foundation model.Nature communications · 2024Article
- Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities.Frontiers in pharmacology · 2024Review
- Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.Journal of chemical information and modeling · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Matched molecular pairs (MMPs) are nowadays a commonly applied concept in drug design. They are used in many computational tools for structure-activity relationship analysis, biological activity prediction, or optimization of physicochemical properties. However, until now it has not been shown in a rigorous way that MMPs, that is, changing only one substituent between two molecules, can be predicted with higher accuracy and precision in contrast to any other chemical compound pair. It is expected that any model should be able to predict such a defined change with high accuracy and reasonable precision. In this study, we examine the predictability of four classical properties relevant for drug design ranging from simple physicochemical parameters (log
Identifiers
What OpenQuestion holds
Registered trials
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.