Machine Learning for Chemistry: Models, Data, and Applications

Representations

SMILES, SELFIES, InChI

Graph-based (atoms as nodes, bonds as edges)

3D coordinates and conformers

Spectra as sequences/images

Model Families

GNNs (MPNN, GCN, GAT), equivariant models (SE(3)-transformers)

Transformers for sequence and graph serialization

Kernel methods (GPs) with uncertainty quantification

Autoencoders and diffusion for generative design

Data and Curation

Ensure consistent protonation, tautomers, stereochemistry, and unit normalization. Deduplicate via scaffold or fingerprint similarity. Track assay drift and batch effects.

Tasks

Property prediction (ADMET, solubility, pKa, spectra)

Reaction prediction and retrosynthesis

Materials discovery (band gaps, conductivity, permeability)

Structure generation and optimization

жива демонстрація · пов'язана симуляція● LIVE

Reliability

Uncertainty (ensembles, MC dropout, GPs)

Applicability domain and out-of-distribution checks

Conformal prediction for calibrated intervals

Active Learning and Experimentation

Close the loop with Bayesian optimization, diversity-aware acquisition, and automated labs. Record negative results and metadata for future reuse.

Deployment

Package models with versioned datasets, unit tests, and monitoring. Use model cards and data sheets. Consider privacy and IP in multi-tenant settings.

Examples

Example 1: Solubility Prediction with a GNN

Curate dataset with standardized units and scaffold split.

Train a GNN baseline; evaluate MAE and calibration.

Deploy with conformal prediction intervals.

Example 2: Retrosynthesis with a Transformer

Tokenize reactions; augment with templates.

Train sequence-to-sequence model; apply beam search.

Validate routes with cost and safety constraints.

Frequently asked questions

Which representation should I start with?

Begin with graphs for molecular tasks; use 3D for geometry-sensitive properties.

How much data do I need?

Hundreds to thousands per task; use transfer learning, multi-tasking, and data augmentation when scarce.

How to avoid data leakage?

Use scaffold splits, time splits, and strict deduplication.

What baseline should I compare to?

Strong linear or tree-based models on fixed fingerprints, plus a simple GNN.

How to quantify uncertainty?

Ensembles and conformal prediction provide calibrated estimates.

How to interpret models?

Attribution on atoms/bonds, counterfactuals, and feature importance; validate with domain knowledge.

How to improve generalization?

Data augmentation, self-supervision, domain-informed features, and rigorous validation splits.

Are generative models production-ready?

Use with constraints and multi-objective scoring; human-in-the-loop remains essential.

How to integrate with ELNs/LIMS?

Expose REST APIs, ensure schema alignment, and record model provenance.

What metrics should I report?

MAE/RMSE/AUC plus calibration, robustness, and fairness analyses; include ablations.

Try it live

Everything above runs in your browser — open Reaction-Diffusion and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Reaction-Diffusion simulation