Computational Drug Discovery

Computational Drug Discovery Part 5 (Subpart 2/3): Generative Models for De Novo Drug Design - Diffusion Models

From prediction to creation (Subpart 2/3): Understanding diffusion models for molecular generation, with detailed implementation of torsional diffusion for 3D conformation generation.

Computational Drug Discovery Part 5 (Part 3/3): Generative Models for De Novo Drug Design - Transformers

From prediction to creation (Part 3/3): : how AI generates novel drug molecules optimized for multiple objectives using autoregressive transformer architectures.

Computational Drug Discovery Part 5 (Subpart 1/3): Generative Models for De Novo Drug Design - VAE and GAN

From prediction to creation (Subpart 1/3): A quick intro to how AI generates novel drug molecules optimized for multiple objectives using VAE and GAN model architectures.

Computational Drug Discovery Part 4: Graph Neural Networks for Molecular Property Prediction

A technical deep-dive into Graph Neural Networks (GNNs) for predicting molecular properties. Learn how to construct molecular graphs, implement message passing architectures, and apply attention mechanisms to drug discovery tasks.

Computational Drug Discovery Part 3 (Subpart 1/3): AlphaFold Overview

How DeepMind’s AlphaFold2 solved the 50-year grand challenge in biology – the protein folding problem – using transformers, evolutionary information, and geometric reasoning and what it means for drug discovery - Subpart 1/3