Link-INVENT is an RNN-based generative model for molecular linker design that uses reinforcement learning with a flexible scoring function, demonstrated on fragment linking, scaffold hopping, and PROTAC design.
PASITHEA adapts deep dreaming from computer vision to molecular design, directly optimizing SELFIES-encoded molecules for target chemical properties via gradient-based inversion of a trained regression network.
Proposes SMI+AIS, a hybrid molecular representation combining standard SMILES tokens with chemical-environment-aware Atom-In-SMILES tokens, demonstrating improved molecular generation for drug design targets.
This foundational paper introduces a variational autoencoder (VAE) that encodes SMILES strings into a continuous latent space, allowing gradient-based optimization of molecular properties. Joint training with a property predictor organizes the latent space by chemical properties, and Bayesian optimization over the latent surface discovers drug-like molecules with improved QED and synthetic accessibility.
DOCKSTRING bundles an AutoDock Vina wrapper, a 260K-molecule docking dataset across 58 protein targets, and pharmaceutically relevant benchmarks for regression, virtual screening, and de novo design.
Identifies failure modes in molecular generative models, showing that trivial edits fool distribution-learning benchmarks and that ML-based scoring functions introduce exploitable model-specific and data-specific biases during goal-directed optimization.
A graph-based genetic algorithm (GB-GA) and a graph-based generative model with Monte Carlo tree search (GB-GM-MCTS) for molecular optimization that match or outperform ML-based generative approaches while being orders of magnitude faster.
GuacaMol provides an open-source benchmarking framework with 5 distribution-learning and 20 goal-directed tasks to standardize evaluation of de novo molecular design models.
MolScore is an open-source framework that unifies scoring functions, evaluation metrics, and benchmarks for generative molecular design, with configurable objectives and GUI support.
A large-scale benchmark of 25 molecular optimization methods on 23 oracles under constrained oracle budgets, showing that sample efficiency is a critical and often neglected dimension of evaluation.
Frey et al. discover empirical power-law scaling relations for both chemical language models (ChemGPT, up to 1B parameters) and equivariant GNN interatomic potentials, finding that neither domain has saturated with respect to model size, data, or compute.
This position paper demonstrates that genetic algorithms (GAs) perform surprisingly well on molecular generation benchmarks, often outperforming complex deep learning methods. The authors propose the GA criterion: new molecule generation algorithms should demonstrate a clear advantage over GAs.