What are the best computational drug discovery project topics?

Top topics include molecular docking with AutoDock/Vina, structure-based virtual screening, QSAR/QSPR modeling, ADMET prediction, molecular dynamics of protein-ligand complexes, pharmacophore modeling, and AI generative models for de novo design.

Do you provide complete project packages?

Yes — protocol notes, docking/QSAR pipelines, evaluation metrics, university-format report, PPT and viva Q&A.

Drug Discovery Computational Projects | Computational Drug Discovery PhD | Drug Discovery Computational Biology

Drug Discovery Computational Biology

Computational drug discovery (CADD) uses structure- and ligand-based methods to prioritise compounds before wet-lab testing — molecular docking, virtual screening, QSAR, ADMET filters, molecular dynamics and increasingly AI generative models. Student projects typically combine open tools (AutoDock Vina, RDKit, GROMACS) with public databases (PDB, ChEMBL, ZINC, PubChem).

Below: 80+ topics with tools and representative datasets.

Drug Discovery Computational Chemistry

Tools & Platforms

AutoDock Vina RDKit GROMACS DeepChem PyMOL ChEMBL · PDB

Best Computational Drug Discovery Project Topics (80+)

Topics with tools and datasets.

#	Project Topic	Tools	Datasets
Molecular Docking
01	DockProtein–Ligand Docking with AutoDock Vina for a Disease Target	AutoDock Vina · PyMOL	PDB · PubChem ligands
02	DockRedocking Validation and RMSD Analysis of Co-Crystal Ligands	Vina · RDKit · PyMOL	PDB co-crystal structures
03	DockFlexible Side-Chain Docking vs Rigid Receptor Comparison	AutoDock · scoring analysis	PDB · known actives
04	DockMulti-Target Docking of Natural Product Library	Vina · batch scripts · RDKit	ZINC natural · ChEMBL
05	DockBlind Docking for Binding Site Identification	Vina · grid search · PyMOL	PDB apo structures
06	DockConsensus Docking: Combining Multiple Scoring Functions	Vina · additional scorers	DUD-E subset
07	DockCovalent Docking Concepts for Reactive Warheads	specialised protocols · literature	Covalent ligand sets
08	DockProtein–Protein Interface Docking for PPI Inhibitors Concepts	docking tools · analysis	PPI benchmark complexes
09	DockDocking-Based SAR Exploration of Analogue Series	Vina · RDKit · plots	ChEMBL analogue series
10	DockWater-Mediated Interactions in Docked Poses Analysis	PyMOL · hydration analysis	PDB high-res structures
Virtual Screening & Hit Identification
11	VSStructure-Based Virtual Screening of ZINC Library vs Target	Vina · RDKit · filtering	ZINC · PDB target
12	VSLigand-Based Virtual Screening with Similarity Search	RDKit · fingerprints · Tanimoto	ChEMBL actives · decoys
13	VSHierarchical VS: Pharmacophore Filter then Docking	Pharmit concepts · Vina	DUD-E · ChEMBL
14	VSEnrichment Factor and ROC Analysis of Virtual Screening	Python · metrics	DUD-E benchmark
15	VSFragment-Based Virtual Screening and Growing Strategies	RDKit · docking	Fragment libraries
16	VSNatural Product Virtual Screening against Kinases / Proteases	Vina · NP libraries	ZINC NP · PDB kinases
17	VSMachine Learning–Boosted Ranking of Docking Hits	scikit-learn · docking scores	Labeled hit/decoy sets
18	VSMulti-Step Filtering: PAINS, ADMET, then Docking	RDKit · PAINS filters	PubChem · ChEMBL
19	VSTarget-Focused Library Design from Known Actives	RDKit · scaffold analysis	ChEMBL target actives
20	VSCross-Docking Study to Assess Pose Prediction Robustness	Vina · multi-structure PDB	PDB conformational set
QSAR / QSPR Modeling
21	QSAR2D QSAR Model for Bioactivity Prediction using RDKit Descriptors	RDKit · scikit-learn	ChEMBL bioactivity set
22	QSAR3D QSAR / CoMFA-Style Concepts with Alignment	alignment tools · regression	Aligned ligand series
23	QSARQSAR for Toxicity Endpoint Prediction	RDKit · classifiers	Tox21 · related sets
24	QSARFeature Importance and Interpretable QSAR with SHAP	SHAP · tree models	ChEMBL curated set
25	QSARScaffold-Split vs Random-Split Validation in QSAR	RDKit · sklearn	ChEMBL series
26	QSARMulti-Task QSAR for Related Activity Endpoints	multi-output ML · RDKit	Multi-assay ChEMBL
27	QSARDescriptor Selection and Overfitting Control in Small Datasets	feature selection · CV	Small activity sets
28	QSARQSPR for Solubility / LogP Prediction Benchmark	RDKit · regression	Public solubility sets
29	QSARGraph Neural Network QSAR with DeepChem	DeepChem · PyTorch	MoleculeNet benchmarks
30	QSARApplicability Domain Analysis for QSAR Models	distance metrics · Python	Training space analysis
ADMET Prediction & Filtering
31	ADMETIn-Silico ADME Property Prediction Pipeline	RDKit · predictive models	Public ADME datasets
32	ADMEThERG Liability and Cardiotoxicity Risk Filtering	classifiers · RDKit	hERG assay data
33	ADMETCYP Inhibition Prediction for Drug–Drug Interaction Risk	ML · descriptors	CYP inhibition sets
34	ADMETBlood–Brain Barrier Permeability Prediction	RDKit · BBB models	BBB public datasets
35	ADMETDrug-Likeness and Rule-of-Five / Beyond Rule Filters	RDKit · Lipinski · QED	PubChem / ChEMBL
36	ADMETPAINS and Aggregator Alerts in Screening Libraries	RDKit · filter catalogs	Screening library sample
37	ADMETIntegrated ADMET Dashboard for Hit Prioritisation	Python · multi-endpoint scores	Multi-property compound set
38	ADMETToxicity Endpoint Multi-Label Classification	DeepChem / sklearn	Tox21
Molecular Dynamics of Drug Targets
39	MDProtein–Ligand MD Simulation and Binding Stability Analysis	GROMACS · PyMOL	PDB complex
40	MDRMSD, RMSF and Hydrogen-Bond Analysis of Docked Complexes	GROMACS analysis tools	MD trajectories
41	MDFree Energy Concepts: MM-PBSA / MM-GBSA on MD Snapshots	gmx_MMPBSA concepts	MD ensembles
42	MDLigand Unbinding / Residence Time Insights from MD	enhanced sampling concepts	Long MD trajectories
43	MDMembrane Protein–Drug Interaction MD Setup	GROMACS · membrane builder	GPCR / ion channel PDB
44	MDConformational Selection vs Induced Fit Case Study	MD · docking comparison	Apo/holo PDB pairs
45	MDWater Network Analysis in Active Sites from MD	trajectory tools · PyMOL	MD water density maps
46	MDForce Field Comparison Impact on Ligand Pose Stability	GROMACS · multiple FF	Same complex multi-FF
AI, Generative Models & Deep Learning
47	AIDeep Learning QSAR with Graph Convolutional Networks	DeepChem · MoleculeNet	MoleculeNet datasets
48	AIGenerative Model for De Novo Molecule Design (VAE / SMILES RNN)	PyTorch · RDKit	ChEMBL SMILES
49	AIProperty-Optimised Molecule Generation with Reinforcement Learning	RL · RDKit · scoring	Reward-guided design set
50	AIDrug–Target Interaction Prediction with Deep Models	PyTorch · DTI frameworks	BindingDB · Davis / KIBA
51	AITransformer Models for Molecular Property Prediction	HF / custom transformers	MoleculeNet
52	AIActive Learning for Efficient Bioactivity Label Acquisition	modAL · QSAR loop	Unlabeled pool + oracle sim
53	AIMulti-Modal Fusion: Structure + Ligand Embeddings for DTI	GNN + protein embeddings	DTI benchmarks
54	AIExplainable AI for Molecular Predictions (Attention / SHAP)	captum · SHAP · RDKit	Trained QSAR / DTI models
Pharmacophore & Ligand-Based Design
55	PharmPharmacophore Model Building from Active Ligand Set	Pharmit / RDKit concepts	ChEMBL actives
56	PharmPharmacophore-Based Virtual Screening and Hit Validation	screening · docking follow-up	ZINC · DUD-E
57	PharmReceptor-Based Pharmacophore from Docked Complexes	pose analysis · features	PDB complexes
58	PharmScaffold Hopping Guided by Pharmacophore Constraints	RDKit · scaffold networks	Analogue series
59	Pharm3D Shape Similarity Screening (ROCS-Style Concepts)	shape tools · RDKit	Query ligands · library
60	PharmConsensus Pharmacophore from Multiple Crystal Structures	multi-structure analysis	PDB multi-ligand target
Target Selection, Pipelines & Capstone
61	AdvDisease Target Prioritisation using Network / Literature Mining	Python · bioinformatics APIs	Open target / literature
62	AdvHomology Modeling of Target Protein for Docking	Modeller concepts · validation	UniProt · templates
63	AdvEnd-to-End CADD Pipeline: VS → Dock → ADMET → Rank	Vina · RDKit · scripts	ZINC + PDB target
64	AdvRepurposing Approved Drugs via Docking against New Target	Vina · DrugBank concepts	Approved drug structures
65	AdvPolypharmacology Prediction: Multi-Target Ligand Profiles	multi-target docking · ML	ChEMBL multi-target
66	AdvBinding Site Comparison and Druggability Assessment	fpocket concepts · PyMOL	PDB pocket set
67	AdvFragment Growing and Linking In Silico Workflow	RDKit · docking cycles	Fragment hits
68	AdvProspective Virtual Screening Case Study with Retrospective Validation	full protocol · metrics	DUD-E or ChEMBL split
69	AdvOpen-Source vs Commercial Tool Comparison on Benchmark Set	multiple tools · ROC/EF	DUD-E
70	AdvReproducible CADD Workflow with Snakemake / Nextflow Concepts	workflow managers · containers	Pipeline config + data
71	AdvCOVID / Antiviral Target Docking Case Study (Historical Benchmark)	Vina · public structures	PDB viral proteins
72	AdvKinase Inhibitor Selectivity Profiling by Multi-Kinase Docking	batch docking · heatmaps	Kinase PDB set
73	AdvNatural Product Priority Ranking with ADMET + Docking Scores	composite scoring · RDKit	NP libraries
74	AdvInteractive Dashboard for Exploring Docking and QSAR Results	Streamlit · Plotly	Project result tables
75	AdvBenchmarking Scoring Functions on a Common Pose Set	scoring comparison · RMSD	PDBBind concepts
76	AdvPrivacy-Preserving / Federated Concepts for Collaborative CADD	architecture discussion	Distributed dataset notes
77	AdvTeaching Package: From SMILES to Ranked Hits Documentation	full lab manual · scripts	Example target + library
78	AdvUncertainty Quantification in Docking and QSAR Predictions	ensemble methods · metrics	Calibration plots
79	AdvMulti-Objective Optimisation: Potency vs ADMET Trade-offs	Pareto analysis · Python	Multi-property compound set
80	AdvCapstone: Full Computational Hit Discovery Report on Chosen Target	Vina · RDKit · GROMACS optional	PDB + ChEMBL + ZINC
81	AdvMetabolite Prediction and Soft-Spot Analysis Concepts	metabolism tools · RDKit	Known drug metabolites
82	AdvOff-Target Prediction and Safety Flagging Pipeline	similarity · panel docking	Off-target panel structures

Datasets are public (PDB, ChEMBL, ZINC, PubChem, DUD-E, MoleculeNet, BindingDB, Tox21). Always cite sources and respect database licences. Contact us for protocols, pipelines, university-format report, PPT and viva Q&A.

Why Choose Us for Computational Drug Discovery Projects?

Bangalore-based guidance for BE, BTech, MTech and bioinformatics students.

Docking & Virtual Screening

AutoDock Vina pipelines, enrichment analysis and multi-step filtering with open libraries.

QSAR & ADMET

Descriptor-based and deep models, toxicity filters and prioritisation dashboards.

MD & Dynamics

GROMACS setups, stability metrics and free-energy concepts for complex validation.

AI & Generative Design

Graph models, SMILES generators and explainable predictions on public benchmarks.

FAQ — Computational Drug Discovery Projects

Strong topics include AutoDock Vina docking and validation, structure-based virtual screening with enrichment metrics, RDKit QSAR, ADMET filtering, GROMACS MD of complexes, pharmacophore screening and deep generative models for molecules.

AutoDock Vina, PyRx, RDKit, Open Babel, GROMACS, PyMOL, DeepChem, scikit-learn; datasets include PDB, ChEMBL, ZINC, PubChem, BindingDB, DUD-E, MoleculeNet and Tox21.

No. Most student projects are fully doable with open-source stacks. Commercial tools are optional if your institution provides licences.

Yes — protocol notes, scripts, evaluation metrics, university-format report, PPT and viva Q&A.

Computational Drug Discovery Lab — Bangalore

Docking, QSAR, MD and AI pipeline support for CADD final-year projects.

Molecular
Docking

AutoDock Vina

Virtual
Screening

ZINC · DUD-E

QSAR / QSPR
Models

RDKit · ML

ADMET
Filtering

Tox · BBB · CYP

MD
Simulation

GROMACS

AI Generative
Design

DeepChem

Pharmacophore
Screening

Ligand-Based

Viva & Report
Support

Documentation