Evidence map›Paper›PMID 40553331›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

AI-Based Drug Discovery and Design for Different Genetic Designs.

Devi Basumatary, Shalini G Devi, Deepsikha Swargiary, Jagat C Borah

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Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

4 authors.

Devi BasumataryChemical Biology Lab-I, Institute of Advanced Study in Science and Technology (IASST), Guwahati, Assam, India.
Shalini G DeviChemical Biology Lab-I, Institute of Advanced Study in Science and Technology (IASST), Guwahati, Assam, India.
Deepsikha SwargiaryChemical Biology Lab-I, Institute of Advanced Study in Science and Technology (IASST), Guwahati, Assam, India.
Jagat C BorahChemical Biology Lab-I, Institute of Advanced Study in Science and Technology (IASST), Guwahati, Assam, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Personalized medicine, also known as precision medicine, is a branch of medicine that considers each patient's unique genetic makeup, lifestyle, and environment to customize treatment plans, preventative measures, and diagnoses. By tailoring medical interventions to individual genetic profiles, personalized medicine maximizes treatment efficacy. Better health outcomes and less trial-and-error in therapy selection result from this method, which guarantees that patients receive medicines that are most likely to work for them. Artificial intelligence-based drug discovery and design is an innovative approach that leverages artificial intelligence (AI) techniques to develop personalized medicines tailored to individual genetic profiles. AI holds immense promise in revolutionizing personalized medicine by harnessing the power of data-driven insights to optimize healthcare delivery, improve patient outcomes, and advance our understanding of disease mechanisms and treatment strategies. AI-based algorithms can be optimized to analyze large-scale, diversified datasets, including genetic information and by integrating and examining numerous data sources, AI can find patterns, correlations, and insights that conventional analytic techniques would miss. AI algorithms are used to interpret complex genomic data, such as DNA sequencing results, to identify genetic variants associated with disease susceptibility, drug response, and treatment outcomes. This chapter aims to analyze the present state of AI-based drug development for various genetic designs, along with the accompanying issues and measures to fully utilize the potential AI holds in personalized medicine.

Indexed as

Artificial IntelligenceDrug DesignDrug DiscoveryPrecision MedicineAlgorithmsGenomicsHumansArtificial intelligence (AI)Disease diagnosisDrug discoveryGenetic profilesPrecision medicines

Identifiers

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Registered trials

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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.