Evidence map›Paper›PMID 40301135›Full record

ReviewMolecular diversity2025

Machine learning: Python tools for studying biomolecules and drug design.

Fedor V Ryzhkov, Yuliya E Ryzhkova, Michail N Elinson

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular diversity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Fedor V RyzhkovN. D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 47 Leninsky Prospekt, 119991, Moscow, Russia. ryzhkovfv@ioc.ac.ru.ORCID http://orcid.org/0000-0002-5844-6019
Yuliya E RyzhkovaN. D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 47 Leninsky Prospekt, 119991, Moscow, Russia.ORCID http://orcid.org/0000-0002-5826-2279
Michail N ElinsonN. D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 47 Leninsky Prospekt, 119991, Moscow, Russia.ORCID http://orcid.org/0000-0002-4647-5410

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing adoption of computational methods and artificial intelligence in scientific research has led to a growing interest in versatile tools like Python. In the fields of medical chemistry, biochemistry, and bioinformatics, Python has emerged as a key language for tackling complex challenges. It is used to solve various tasks, such as drug discovery, high-throughput and virtual screening, protein and genome analysis, and predicting drug efficacy. This review presents a list of tools for these tasks, including scripts, libraries, and ready-made programs, and serves as a starting point for scientists wishing to apply automation or optimization to routine tasks in medical chemistry and bioinformatics.

Indexed as

Drug DesignMachine LearningProgramming LanguagesSoftwareComputational BiologyDrug DiscoveryHumansBiochemistryBioinformaticsComputational chemistryMedical chemistryProgrammingPython

Identifiers

PMID40301135

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.