Phytocompounds/ Phytochemistry/ Natural
Compound-Based Projects
Project1:AI-AssistedDesigningofNovel AnticancerCompounds
Project1:AI-AssistedDesigningofNovelAnticancerCompounds
This project focused on utilizing artificial intelligence (AI) and cheminformatics to design, screen, and prioritize novel anticancer compounds targeting oncogenic proteins (e.g., EGFR, BCL2). The pipeline combined deep learning-based molecular generation, molecular docking, ADMETprediction,andQSARmodelingtoidentifyhigh-potentialdrugcandidates.
AdvancedMethodologyPipeline 2
CompoundGenerationUsingAI
• EmployedGenerativeAdversarialNetworks(GANs)trainedon ChEMBLanticancermolecules.
• Generated>1000virtualmoleculesandfilteredby:
⚬ Structuraldiversity
⚬ Drug-likenessfilters(Lipinski'srules)
ADMETPropertyPrediction
• UsedSwissADME,pkCSM,andADMETlabtoevaluate:
⚬ Absorption
⚬ Distribution
⚬ Metabolism
⚬ Excretion
⚬ Toxicity
Docking&VirtualScreening
• TopcompoundsdockedwithAutoDockVinaagainsttarget protein(e.g.,EGFR).
• InteractionresiduesvisualizedwithPyMOLandLigPlot+.
QSARModeling
• ExtractedmoleculardescriptorsviaPaDEL.
• ModeledbiologicalactivityusingRandomForestand SupportVectorRegression(SVR).
• InterpretedfeaturecontributionusingSHAPvalues
ProjectOutcomes
Threeleadcompounds(Lead_1,Lead_2, Lead_3)outperformedcontroldrugindocking scores.
ADMETprofilingconfirmedgood pharmacokineticbehavior
QSARanalysispredictedhighinhibitorypotential basedonmoleculardescriptors.
Alltopleadsshowed:
⚬ Highbindingaffinity(<-9kcal/mol)
⚬ Favorablebioavailabilityandsafetyprofile
AllAI-designedcompoundsshowed strongerbindingthanthecontrol drug.
ADMETprofileofLead_1indicating astrongbalanceofbioavailability,low toxicity,andgoodmetabolicstability.
Descriptivechemicalproperties
(MolWeight,LogP,TPSA)oflead compounds.
Conclusion
ThisprojectdemonstratedthesuccessfuluseofAI-assisted compounddesignintegratedwithbioinformaticsand cheminformaticstoolsforanticancerdrugdiscovery.
Achievements
AI-poweredlead discoverypipelinefully established. Producedviable candidateswith therapeuticpotential. Generatedacomprehensive insilicoprofileforlead optimizationandpreclinical testing.
