Surveys and Reviews

A review chapter tracing three stages of transformer adoption in chemistry: task-specific single-modality models (reaction prediction, retrosynthesis), multimodal approaches bridging spectra and text, and LLM-powered agents like ChemCrow for general chemical reasoning.

A minireview of chemical language models for de novo molecule design, covering SMILES and SELFIES representations, RNN and Transformer architectures, distribution learning, goal-directed and conditional generation, and prospective experimental validation.

This perspective from Choi et al. reviews foundation models in chemistry, categorizing them as ‘small’ (domain-specific, e.g., property prediction, MLIPs, inverse design) and ‘big’ (multi-domain, e.g., multimodal and LLM-based). It surveys pretraining strategies, key architectures (GNNs and language models), and outlines future directions for scaling, efficiency, and interpretability.

This survey organizes generative AI for de novo drug design into two themes: small molecule generation (target-agnostic, target-aware, conformation) and protein generation (structure prediction, sequence generation, backbone design, antibody, peptide). It covers four generative model families (VAEs, GANs, diffusion, flow-based), catalogs key datasets and benchmarks, and provides 12 comparative benchmark tables across all subtasks.

A foundational review surveying how deep generative models (VAEs, GANs, reinforcement learning) enable inverse molecular design, covering molecular representations, chemical space navigation, and applications from drug discovery to materials engineering.

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.

Jiang et al. survey 12 families of transformer architectures in molecular science, covering GPT, BERT, BART, graph transformers, Transformer-XL, T5, ViT, DETR, Conformer, CLIP, sparse transformers, and mobile/efficient variants, with detailed algorithmic descriptions and molecular applications.

PRISMA-based systematic review of 72 papers on chemical language models for molecular generation, comparing architectures and biased methods using MOSES metrics.

A comprehensive review of transformer-based chemical language models operating on SMILES, categorizing encoder-only (BERT variants), decoder-only (GPT variants), and encoder-decoder models with analysis of tokenization strategies, pre-training approaches, and future directions.

Sultan et al. review 16 sequence-based transformer models for molecular property prediction, systematically analyzing seven design decisions (database selection, chemical language, tokenization, positional encoding, model size, pre-training objectives, and fine-tuning strategy) and identifying a critical need for standardized evaluation practices.

MolGenSurvey systematically reviews ML models for molecule design, organizing the field by molecular representation (1D/2D/3D), generative method (deep generative models vs. combinatorial optimization), and task type (8 distinct generation/optimization tasks). It catalogs over 100 methods, unifies task definitions via input/output/goal taxonomy, and identifies key challenges including out-of-distribution generation, oracle costs, and lack of unified benchmarks.