Target2DeNovoDrug

Target2DeNovoDrug performs de novo molecule generation, optimization, and in silico evaluation for target-specific drug discovery.

Key Features:

• Automated Drug Discovery Workflow: Integrates AI-driven and in silico techniques into an automated workflow for lead generation and evaluation.
• Target-Specific Design: Accepts target signatures as amino acid or nucleotide sequences to focus candidate generation on specified targets.
• Data-Driven QSAR Modeling: Builds QSAR models using PubChem data for prediction and initial lead generation.
• De Novo Molecule Generation: Generates novel molecules using a generative Long Short-Term Memory (LSTM) model.
• Optimization for Drug-Likeness: Optimizes generated molecules using the DeepFMPO deep learning model to enhance drug-like properties.
• In Silico Docking: Performs automated docking with AutoDock-Vina to assess protein–ligand binding affinities.
• Molecular Dynamics Simulation: Executes Molecular Dynamics protocols on selected protein–ligand complexes to evaluate stability and dynamics.

Scientific Applications:

• Structure-based de novo drug design: Facilitates generation and evaluation of novel compounds for structure-based drug discovery workflows.
• Anti-inflammatory target discovery: Supports lead generation and assessment against targets such as Tumor Necrosis Factor-Alpha for anti-inflammatory research.

Methodology:

Protocols explicitly used include Target2Drug (QSAR modeling based on PubChem data), Target2DeNovoDrug (generative LSTM molecule generation), Target2DeNovoDrugPropMax (DeepFMPO optimization), and AutoDock-Vina plus Molecular Dynamics for docking and dynamic simulation.