ArticleACS pharmacology & translational science2026
Decoding of Inconsistent Biological Data: A Critical Step toward Enhanced AI Predictivity in Drug Discovery.
Article in ACS pharmacology & translational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combining bioactivity data of assays against the same target, which are obtained from different sources, was recently shown to lead to considerable noise for training data sets of machine learning (ML) models. In this Viewpoint, we address the profound impact originating from often overlooked changes to an assay protocol relating to the buffer composition and experimental setup. We cover two examples of protein targets that undergo conformational changes driven by extrinsic factors: enzymes as catalytically active proteins, and viral surface proteins as structural targets. We discuss strategies to tackle this challenge for the case of enzyme inhibitors/binders, the utility of models based on deep learning (DL), and current limitations of computational studies assessing protein-ligand interactions. In an interview with an expert in the field of large language models (LLMs) and agentic AI, we explore how the latest developments in these areas can be leveraged to support drug discovery efforts.
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.