Скачать или смотреть From Trial-and-Error to GenAI: How Drug-Target Prediction Transformed Pharma

Pitchworks GCC -and Pitchworks VC Studio Group has built. Trial-and-Error to GenAI: How Drug-Target Prediction Transformed Pharma

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What once took years of lab experiments and trial-and-error in pharmaceutical R&D now takes hours with Generative AI. This video explores how Drug-Target Interaction (DTI) prediction has evolved—transitioning from manual testing and basic simulation tools to AI-first platforms powered by graph neural networks (GNNs), transformers, and deep generative models.
Traditional Era:
Manual lab-based screening, slow wet-lab assays, physical compound libraries, and limited computational support.

💻 Early Digital Era:
Use of basic QSAR models, early molecular docking software like AutoDock and SwissDock, and structure-based drug design tools—still time-consuming and limited in prediction accuracy.

🤖 GenAI-Powered Era:
Advanced Generative AI tools now automate molecule design, predict protein-ligand binding affinity, simulate 3D structures, and even optimize ADMET properties—all with cloud computing and real-time AI inference.

🔍 What You’ll Learn in This Video:
How DTI prediction used to work without AI or computers

Key turning points in computational drug discovery

The role of AlphaFold, DeepChem, MolBERT, and REINVENT in modern pharma pipelines

Real-world benefits of AI in drug development: faster hit discovery, lower R&D costs, and smarter molecule design

Case studies from Insilico Medicine and BenevolentAI using GenAI to accelerate drug development

📖 Read the Full Blog
Explore a deep dive into how GenAI is shaping the future of pharma, the tools behind it, and real-world impact stories:
🔗 https://www.pitchworks.club/post/gene...

🛠️ Tools & Platforms Mentioned
AlphaFold 2 – AI-powered protein structure prediction

DeepChem – Open-source AI framework for drug discovery

REINVENT – Molecule generation platform using reinforcement learning

GraphDTA – GNN-based DTI predictor

MolBERT / ChemBERTa – Language models for chemical representations

AutoDock Vina – Docking simulation tool

SwissDock – Web-based docking tool

RDKit – Chemoinformatics toolkit for molecule manipulation

📊 Why This Matters
Speed: GenAI reduces lead compound identification from months to days

Precision: Improved DTI prediction leads to fewer failures in late-stage clinical trials

Innovation: Enables design of first-in-class drugs for rare or untargeted diseases

Cost-saving: Cuts down physical testing and screening overhead by 60–80%

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Generative AI, Drug discovery, DTI prediction, Drug-target interaction, AI in healthcare, Graph neural networks, Bioinformatics, Deep learning,
AlphaFold, DeepChem, Molecule generation, MedTech, Computational biology, Drug repurposing, REINVENT AI, AI in pharma, AI drug design, AI molecule prediction, Protein folding, AI in drug development, Molecular docking, Life sciences AI, Drug design tools, QSAR, Drug-target binding, Healthcare AI, AI biotech, Target prediction, ChemBERTa, MolBERT, Machine learning in drug discovery
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