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.
Group SELFIES extends SELFIES with group tokens representing functional groups and substructures, maintaining chemical robustness while improving distribution learning and molecular generation quality.
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.
Lingo3DMol introduces FSMILES, a fragment-based SMILES representation with local and global coordinates, to generate drug-like 3D molecules in protein pockets via a transformer language model.
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.
Proposes Captions as Representations (CaR), where ChatGPT generates textual explanations for SMILES strings that are then used to fine-tune small language models for molecular property prediction.
Demonstrates that standard transformer language models, trained with next-token prediction on sequences from XYZ, CIF, and PDB files, can generate valid 3D molecules, crystals, and protein binding sites competitive with domain-specific 3D generative models.
MaCBench evaluates frontier vision language models across 1,153 chemistry and materials science tasks spanning data extraction, experimental execution, and data interpretation, uncovering fundamental limitations in spatial reasoning and cross-modal integration.
MolBERT pre-trains a BERT model on SMILES strings using masked language modeling, SMILES equivalence, and physicochemical property prediction as auxiliary tasks, achieving state-of-the-art results on virtual screening and QSAR benchmarks.
MolPMoFiT applies ULMFiT-style transfer learning to QSAR modeling, pre-training an AWD-LSTM on one million ChEMBL molecules and fine-tuning for property prediction on small datasets.
nach0 unifies natural language and SMILES-based chemical tasks in a single encoder-decoder model, achieving competitive results across molecular property prediction, reaction prediction, molecular generation, and biomedical NLP benchmarks.