A critical reassessment of the PMO benchmark for de novo molecule generation, showing that adding molecular weight, LogP, and diversity filters substantially re-ranks generative models, with Augmented Hill-Climb emerging as the top method.
Overview of REINVENT 4, an open-source generative molecular design framework from AstraZeneca that unifies RNN and transformer generators within reinforcement learning, transfer learning, and curriculum learning optimization algorithms.
An early and influential review cataloging 45 papers on deep generative modeling for molecules, comparing RNN, VAE, GAN, and reinforcement learning architectures across SMILES and graph-based representations.
Compares RNN-based and Transformer-based chemical language models across three molecular generation tasks of increasing complexity, finding that RNNs excel at local features while Transformers handle large molecules better.
This paper introduces structured state space sequence (S4) models to chemical language modeling, showing they combine the strengths of LSTMs (efficient recurrent generation) and GPTs (holistic sequence learning) for de novo molecular design.
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.
Introduces SMILES Pair Encoding (SPE), a data-driven tokenization algorithm that learns high-frequency SMILES substrings from ChEMBL to produce shorter, chemically interpretable token sequences for deep learning.
SPMM pre-trains a dual-stream transformer on SMILES and 53 molecular property vectors using contrastive learning and cross-attention, enabling bidirectional structure-property generation, property prediction, and reaction prediction through a single model.
PRISMA-based systematic review of 72 papers on chemical language models for molecular generation, comparing architectures and biased methods using MOSES metrics.
t-SMILES represents molecules by fragmenting them into substructures, building full binary trees, and traversing them breadth-first to produce SMILES-type strings that reduce nesting depth and outperform SMILES, DeepSMILES, and SELFIES on generation benchmarks.
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.
X-MOL applies large-scale Transformer pre-training on 1.1 billion molecules with a generative SMILES-to-SMILES strategy, then fine-tunes for five molecular analysis tasks including property prediction, reaction analysis, and de novo generation.