Evidence map›Paper›PMID 40143396›Full record

ArticleMedicinal chemistry (Shariqah (United Arab Emirates))2025

Integrated Artificial Intelligence and Physics-Based Methods for the

Atul Darasing Pawar, Heba Taha M Abdelghani, Hemchandra Deka, Monishka Srinivas Battula, Surajit Maiti, Pritee Chunarkar Patil, Shovonlal Bhowmick, Rupesh V Chikhale

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Article in Medicinal chemistry (Shariqah (United Arab Emirates)), 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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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

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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

8 authors.

Atul Darasing PawarDepartment of Discovery Science, SilicoScientia Private Limited, Nagananda Commercial Complex, No. 07/3, 15/1, 18th Main Road, Jayanagar 9th Block, Bengaluru - 560041, India.
Heba Taha M AbdelghaniDepartment of Exercise Physiology, College of Sport Sciences and Physical Activity, King Saud University, Riyadh 11451, Saudi Arabia.
Hemchandra DekaDepartment of Discovery Science, SilicoScientia Private Limited, Nagananda Commercial Complex, No. 07/3, 15/1, 18th Main Road, Jayanagar 9th Block, Bengaluru - 560041, India.
Monishka Srinivas BattulaDepartment of Discovery Science, SilicoScientia Private Limited, Nagananda Commercial Complex, No. 07/3, 15/1, 18th Main Road, Jayanagar 9th Block, Bengaluru - 560041, India.
Surajit MaitiGCC Biotech India Pvt Ltd. 15, 1st Ave Rd, AB Block, Sector 1, Bidhannagar, Kolkata, West Bengal 700053, India.
Pritee Chunarkar PatilDepartment of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth Deemed to be University, Pune-Satara Road, Pune, India.
Shovonlal BhowmickDepartment of Discovery Science, SilicoScientia Private Limited, Nagananda Commercial Complex, No. 07/3, 15/1, 18th Main Road, Jayanagar 9th Block, Bengaluru - 560041, India.
Rupesh V ChikhaleDepartment of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College London, London, UK.ORCID 0000-0001-5622-3981

Funding

Dept. of Biotechnology, Govt. Of India BT/INF/22/SP41297/2021King Saud University, Riyadh, Saudi Arabia RSPD2024R756
6 · The paper itself

Abstract

introductionSYK (Spleen Tyrosine Kinase) regulates immune response and is a promising target for cancer, sepsis, and allergy therapies. This study aims to create novel compounds that serve as alternative inhibitors for cancer treatments targeting SYK.

methodsA thorough combination of machine learning (ML) and physics-based methods was employed to achieve these goals, encompassing

resultsA total of 5576 novel molecules with key pharmacophoric features were generated using an ML-driven de novo approach against 21 diaminopyrimidine carboxamide analogs. Pharmacokinetic and toxicity evaluation assisted by the ML approach revealed that 4353 chemical entities fulfilled the acceptable pharmacokinetic and toxicity profiles. By screening through binding energy threshold from the physics-based multitier molecular docking, and ML-assisted absolute binding affinity identified the top four molecules such as RI809 (2-([1,1'-biphenyl]-3-ylmethyl)-4-((2- aminocyclohexyl)oxy)benzamide), RI1393 (4-((2-aminocyclohexyl)amino)-2-(3-(1-methyl-1Hpyrazol- 5-yl)-4-(trifluoromethyl)benzyl)benzamide), RI2765 (2-([1,1'-biphenyl]-3-ylmethyl)-4-((4- aminocyclohexyl)methyl)benzamide), and RI3543 (2-([1,1'-biphenyl]-2-ylmethyl)-4-(piperidin-3- yloxy)benzamide). The final molecules identified exhibit a strong affinity for SYK, attributed to their structural diversity and notable pharmacophoric characteristics. All-atom MD simulations showed that each final molecule retained significant binding interactions with SYK and stability in dynamic states, indicating their potential as anticancer agents. Calculated binding free energy for selected molecules using molecular mechanics with generalized Born and surface area (MMGBSA) ranged from -6 to -35 kcal/mol, indicating strong SYK affinity.

conclusionIn conclusion, the integration of AI and physics-based methods successfully developed promising SYK inhibitors with significant potential. The molecules reported could be vital anticancer agents subjected to experimental validation.

Indexed as

Artificial IntelligenceDrug DesignProtein Kinase InhibitorsSyk KinaseHumansMachine LearningMolecular Docking SimulationMolecular Dynamics SimulationMolecular StructurePyrimidinesStructure-Activity RelationshipProtein Kinase InhibitorsPyrimidinesSyk KinaseSYK protein, humanDe novo designmachine learningmolecular dockingmolecular dynamics simulationspleen tyrosine kinasevirtual screening.

Identifiers

PMID40143396

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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.