ArticleMethods in molecular biology (Clifton, N.J.)2026
Computational Strategies for Discovering Inhibitors Targeting Therapeutic Enzymes.
Article in Methods in molecular biology (Clifton, N.J.), 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational strategies have exponentially accelerated the process of drug discovery. Varied algorithms have been established ranging from identification of potential drug target to hit molecule. The utilization of these computational algorithms has effectively reduced the cost and time while enhancing the accuracy of prediction. This chapter provides a comprehensive procedure focusing on a combination of structure-based and ligand-based approaches for discovering inhibitors for development of therapies. Identification of drug target using comparative genomics and network-based approach, AI-based molecular modeling, ligand-based pharmacophore modeling along with structure-based molecular docking to perform virtual screening of large compound libraries, molecular dynamics simulation, and end-state free energy calculations are the key methods discussed. This piece of work will act as an effective guide for utilizing different computational platforms for successful drug discovery.
Indexed as
Identifiers
42562986What 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.