Developing AI System for Pharmaceuticals — Drug Discovery Assistant
New drug development takes 10–15 years and costs $2.6B (DiMasi et al.). AI shortens this path not through magic, but by reducing failed experiments through better prediction.
Drug Discovery Stages Where AI Works
Target Identification
Identifying proteins or genes associated with disease. AI analyzes:
- Omics data (genomics, proteomics, transcriptomics)
- Literature mining: millions PubMed publications — GNN reveals hidden gene-disease-drug relationships
- Protein-protein interaction networks
Hit Identification
Searching for candidate molecules from libraries of 10⁶–10⁹ compounds. Task: predict which molecules will bind to target protein.
Approaches:
- Virtual screening: molecular docking with ML scoring function instead of slow physical simulation
- Generative design: VAE/Diffusion models generate new molecules de novo with specified properties
- Graph Neural Networks: molecules as molecular graphs, activity prediction
Lead Optimization
Converting hit molecule to drug-like candidate: optimizing activity, selectivity, pharmacokinetics. Multi-task learning on combined datasets ChEMBL, PubChem, ExCAPE.
Molecular GNNs
Molecule = graph: atoms (nodes) + chemical bonds (edges). Node features: atomic number, charge, hybridization, degree. Edge features: bond type, aromaticity, ring membership.
import torch
from torch_geometric.nn import GCNConv, global_mean_pool
class MolecularGNN(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = GCNConv(in_channels=9, out_channels=64)
self.conv2 = GCNConv(64, 64)
self.conv3 = GCNConv(64, 128)
self.fc1 = torch.nn.Linear(128, 64)
self.fc2 = torch.nn.Linear(64, 1) # binding affinity prediction
def forward(self, x, edge_index, batch):
x = F.relu(self.conv1(x, edge_index))
x = F.relu(self.conv2(x, edge_index))
x = F.relu(self.conv3(x, edge_index))
x = global_mean_pool(x, batch)
x = F.relu(self.fc1(x))
return self.conv3(x) # predicted pIC50
Benchmarks: QM9 (quantum chemical properties), MoleculeNet, TDC (Therapeutics Data Commons).
ADMET Prediction
Absorption, Distribution, Metabolism, Excretion, Toxicity — over 50% of clinical trial candidates fail due to ADMET issues. Early prediction saves years.
Predicted properties:
- Oral bioavailability (F%)
- Blood-brain barrier permeability
- CYP450 inhibition (drug interactions)
- hERG cardiac toxicity
- Ames test (genotoxicity)
- Aqueous solubility
Dataset: proprietary pharma data + public (ChEMBL, DrugBank). Models: graph-based (better for structural predictions) + fingerprint-based (Morgan, ECFP + GBM).
Generative Molecular Design
REINVENT (AstraZeneca)
RL-based generator of new molecules: prior (RNN or Transformer, trained on ChEMBL) + scoring function (ADMET, activity) → agent generates molecules maximizing reward.
Diffusion Models for 3D Molecules
DiffSBDD, TargetDiff generate 3D conformations accounting for binding pocket shape. Drug design "bottom-up" from target shape.
Fragment-based Design
Combining known fragments with desired properties. AI predicts fragment compatibility and synthetic accessibility (Synthetic Accessibility Score).
Practical Results
- Galunisertib (Eli Lilly): AI cut virtual screening from 9 months to 4 weeks
- AlphaFold2: protein structure prediction → basis for structure-based drug design
- Insilico Medicine: first AI-designed candidate in Phase II clinical trials (2023)
AI doesn't replace chemists — it guides experiments toward higher success probability. Reduction in experimental cycle: 30–50% fewer syntheses to find lead compound.