Fundamental Chemistry for AI Drug Development

 

Fundamental Chemistry for AI Drug Development

Goal: Learn the exact chemistry AI teams use to design, 

predict, 

and filter new drugs. 

No fluff.

Full Curriculum: 6 Modules | 8 Weeks*

Module 1: Molecules as Data - Week 1

Goal: Teach AI to "read" chemistry

- *Atoms,

Bonds, 

Functional Groups:

OH, 

NH2, 

COOH, 

aromatic rings, 

halogens

SMILES: Text for molecules. `CC(=O)OC1=CC=CC=C1C(=O)O` = Aspirin

Molecular Graphs 

Atoms = nodes, 

Bonds = edges. 

This is what GNNs eat

3D Structure: Conformations, 

Chirality R/S. 

Why shape matters for binding

Lab

RDKit → Convert drug name to SMILES to Graph

Module 2: Drug-Likeness Properties - Week 2*

lhh44

Goal: What makes a molecule a drug vs poison

Physicochemical: 

MW, 

logP, 

TPSA, 

H-bond donors/acceptors

Lipinski's Rule of 5: `MW<500, 

logP<5, 

HBD<5, 

HBA<10` → Good oral drug

QED Score: 

Overall "drug-likeness" 0 to 1

Solubility + Permeability: Will it get into blood?

Lab: Calculate all properties for 10,000 drugs from ChEMBL

Module 3: 

Medicinal Chemistry for AI - Week 3-4

Goal: How chemists actually design drugs

SAR: Structure-Activity Relationship. Change 1 atom → 100x better binding

Pharmacophores: The 3D features needed to bind: H-bond donor, aromatic, hydrophobic

Bioisosteres: 

Swap `-COOH` with `-tetrazole`. 

Same effect, 

better PK

Metabolic Hotspots: 

Where liver enzymes attack. Block with F or Cl

PAINS: "Bad" fragments that give false positives

Case Study: How Paxlovid was optimized by AI + chemists

 Module 4: Cheminformatics - Week 5

Goal: The math of molecules

Fingerprints: ECFP4, MACCS keys. Turn molecule → 2048-bit vector

Similarity: Tanimoto. "Find molecules like this known drug"

Diversity: Make sure AI doesn't generate 1000 versions of the same thing

Databases: ChEMBL, PubChem, ZINC20, BindingDB, PDB

Lab: Build a "find similar drugs" AI in Python

Module 5: Chemistry + AI Models - Week 6-7

Goal: Connect chemistry to deep learning

Representation for AI: 

    - SMILES → Transformers like ChemBERTa

    - Graph → GNNs like Chemprop, DMPNN  

    - 3D → Equivariant GNNs for docking

Generative Chemistry: VAEs, GANs, Diffusion Models → Design new molecules

Property Prediction: Train model to predict logP, hERG, solubility

Reaction Prediction: Can we actually synthesize this? Use USPTO data

Lab: Train model to predict if molecule crosses BBB

Module 6: Capstone Project - Week 8*

*Pick 1*:

1.ADMET Filter: Build AI that filters 1M ZINC molecules to 1000 drug-like ones

2.Lead Optimization: Take a weak binder → Use AI to generate 100 better analogs

3.De Novo Design: Design new inhibitors for a protein target from PDB

Deliverable: Jupyter notebook + 5 generated molecules + property report

The 20 Functional Groups AI Must Know

Group SMILES Why AI cares

Alcohol `O` H-bond, solubility

Amine `N` Binding, basicity

Carboxylic Acid `C(=O)O` Solubility, salt form

Amide `C(=O)N` Very common in drugs

Aromatic Ring `c1ccccc1` Hydrophobic, stacking

Core Tools You’ll Use

Tool Used For

RDKit Molecule manipulation, descriptors

DeepChem GNNs + Property prediction

Chemprop State-of-the-art property models

ChEMBL Free bioactivity data

Key Formula for AI

Drug Score = w1*Binding + w2*Solubility + w3*Toxicity + w4*Synthesizability

AI optimizes this score.

Want me to turn this into a *cream + blue A4 "Chemistry for AI Drug Dev Playbook"* with:

1. *SMILES cheat sheet + RDKit code*

2. *List of 50 free datasets to train models*

3. *3 Capstone project templates for your CV*

Which one should I build first for you?