Evidence map›Paper›PMID 41922433›Full record

ArticleScientific reports2026

Unlocking the potential of marine natural product fragments for rational anticancer drug design: a computational approach.

Marineil C Gomez, Kavitha Rajendran, Lemmuel L Tayo

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Marineil C GomezSchool of Chemical, Biological and Materials Engineering and Sciences, Mapua University, 1002, Manila, Philippines. mcgomez@mapua.edu.ph.
Kavitha RajendranSchool of American Education (SAE), Sunway University, 47500, Subang Jaya, Selangor, Malaysia.
Lemmuel L TayoDepartment of Biology, School of Health Sciences, Mapua University, 1205, Makati, Metro Manila, Philippines. lltayo@mapua.edu.ph.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Marine natural products (MNPs) offer a vast and diverse reservoir of chemically rich compounds that remain underexplored for fragment-based drug discovery (FBDD) against cancer. In this study, we developed Marine-FL, a fragment library derived from the Comprehensive Marine Natural Products Database (CMNPD) and applied it within an in silico FBDD pipeline targeting four proteins, CLU, PD-1, PD-L1, and CTLA-4, which are implicated in chemoresistance and immune evasion. CMNPD molecules were standardized, filtered using Rule-of-Three criteria, and systematically fragmented with the BRICS algorithm, followed by deduplication and capping to generate a 4,643-fragment library for chemical space analysis. The library was screened using AutoDock Vina and rescored with the neural network-based AKScore2 model to prioritize fragments for each receptor for the subsequent steps. Top candidates were further optimized by DeepFrag-guided fragment growing, and a representative complex underwent explicit-solvent MD simulations and MM/PB(GB)SA binding free energy calculations to assess stability and relative affinity. PCA and UMAP analyses showed that the fragment library spans broad and sparsely clustered regions, with Murcko scaffold diversity metrics indicating both recurrent cores and a high proportion of rare singletons, indicating a balance between familiar chemotypes and a considerable reserve of structurally novel fragments. Notably, certain multi-ring, conjugated scaffolds recurred among computationally top-ranked fragments across the four target proteins, revealing structural motifs that merit experimental investigation for polypharmacology-directed validation. MD simulations and binding energy calculations of Ligand 10 with PD-L1 provided proof-of-concept dynamic stability at triplicate 200-ns runs and favorable binding energetics with MMPBSA mean delta ΔG = - 8.1 to - 5.6 kcal/mol. Exploratory single-trajectory simulations with PD-1, CTLA-4, and sCLU showed stable complex formation, supporting computational multi-target compatibility pending experimental validation. These findings support the utility of diverse marine-derived fragment libraries as sources of scaffolds with broad applicability, advancing sustainable, structure-guided approaches in anticancer FBDD.

Indexed as

Antineoplastic AgentsAquatic OrganismsBiological ProductsDrug DesignB7-H1 AntigenCTLA-4 AntigenDrug DiscoveryHumansMolecular Docking SimulationMolecular Dynamics SimulationProgrammed Cell Death 1 ReceptorAntineoplastic AgentsB7-H1 AntigenBiological ProductsCTLA-4 AntigenProgrammed Cell Death 1 ReceptorCancer chemoresistanceClusterinFragment-based drug discoveryimmune checkpoint inhibitorMarine natural productsProgrammed cell death

Identifiers

PMID41922433
PMCPMC13180984

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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.