ArticleChemMedChem2013
A chemogenomic analysis of ionization constants--implications for drug discovery.
Article in ChemMedChem, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed, 53 citations in OpenAlex.
- Click Chemistry-Enabled Parallel Synthesis ofMolecules (Basel, Switzerland) · 2026Article
- From NMR to AI: FusingJournal of chemical information and modeling · 2025Article
- Synthesis of tertiary alkyl amines via photoinduced copper-catalysed nucleophilic substitution.Nature chemistry · 2025Article
- GR-pKa: a message-passing neural network with retention mechanism for pKa prediction.Briefings in bioinformatics · 2024Article
- Kinetically-Controlled Ni-Catalyzed Direct Carboxylation of Unactivated Secondary Alkyl Bromides without Chain Walking.Journal of the American Chemical Society · 2024Article
- Natural Lipid Extracts as an Artificial Membrane for Drug Permeability Assay: In Vitro and In Silico Characterization.Pharmaceutics · 2023Article
- Overview of the SAMPL6 pKJournal of computer-aided molecular design · 2021Article
- In vitro pH dependent passive transport of ketoprofen and metformin.ADMET & DMPK · 2021Article
- Dynamic Protonation Dramatically Affects the Membrane Permeability of Drug-like Molecules.Journal of the American Chemical Society · 2019Article
- Molecular Dynamics Simulations of Membrane Permeability.Chemical reviews · 2019Review
- High throughput methods to measure the propensity of compounds to form intramolecular hydrogen bonding.MedChemComm · 2017Review
- Monoselective o-C-H Functionalizations of Mandelic Acid and α-Phenylglycine.Journal of the American Chemical Society · 2015Article
- The CARLSBAD database: a confederated database of chemical bioactivities.Database : the journal of biological databases and curation · 2013Article
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 at 2 institutions in 2 countries.
Funding
Abstract
Chemogenomics methods seek to characterize the interaction between drugs and biological systems and are an important guide for the selection of screening compounds. The acid/base character of drugs has a profound influence on their affinity for the receptor, on their absorption, distribution, metabolism, excretion and toxicity (ADMET) profile and the way the drug can be formulated. In particular, the charge state of a molecule greatly influences its lipophilicity and biopharmaceutical characteristics. This study investigates the acid/base profile of human small-molecule drugs, chemogenomics datasets and screening compounds including a natural products set. We estimate the acid-ionization constant (pK(a)) values of these compounds and determine the identity of the ionizable functional groups in each set. We find substantial differences in acid/base profiles of the chemogenomic classes. In many cases, these differences can be linked to the nature of the target binding site and the corresponding functional groups needed for recognition of the ligand. Clear differences are also observed between the acid/base characteristics of drugs and screening compounds. For example, the proportion of drugs containing a carboxylic acid was 20 %, in stark contrast to a value of 2.4 % for the screening set sample. The proportion of aliphatic amines was 27 % for drugs and only 3.4 % for screening compounds. This suggests that there is a mismatch between commercially available screening compounds and the compounds that are likely to interact with a given chemogenomic target family. Our analysis provides a guide for the selection of screening compounds to better target specific chemogenomic families with regard to the overall balance of acids, bases and pK(a) distributions.
Indexed as
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.