CogMol: Controlled Molecule Generation for COVID-19
CogMol uses a SMILES VAE and multi-attribute controlled sampling (CLaSS) to generate novel, target-specific drug molecules for unseen SARS-CoV-2 proteins without model retraining.
Method Papers: New Algorithms, Architectures, and Mechanisms
Papers whose primary contribution is a new algorithm, architecture, or approximation for AI and physical sciences research.
Curriculum Learning for De Novo Drug Design (REINVENT)
Introduces curriculum learning to the REINVENT de novo design platform, decomposing complex drug design objectives into simpler sequential tasks that accelerate agent convergence and improve output quality over standard reinforcement learning.
DeepSMILES: Adapting SMILES Syntax for Machine Learning
DeepSMILES replaces paired parentheses and ring closure symbols in SMILES with a postfix notation and single ring-size digits, making it easier for generative models to produce syntactically valid molecular strings.
DrugEx v3: Scaffold-Constrained Graph Transformer
DrugEx v3 extends scaffold-constrained drug design by introducing a Graph Transformer with adjacency-matrix-based positional encoding, achieving 100% molecular validity and high predicted affinity for adenosine A2A receptor ligands.
Evolutionary Molecular Design via Deep Learning + GA
An evolutionary molecular design framework that evolves ECFP fingerprint vectors using a genetic algorithm, reconstructs valid SMILES via an RNN decoder, and evaluates fitness with a DNN property predictor.
Group SELFIES: Fragment-Based Molecular Strings
Group SELFIES extends SELFIES with group tokens representing functional groups and substructures, maintaining chemical robustness while improving distribution learning and molecular generation quality.
Lingo3DMol: Language Model for 3D Molecule Design
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: RL-Driven Molecular Linker Generation
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.
LLM-Prop: Predicting Crystal Properties from Text
LLM-Prop uses the encoder half of T5, fine-tuned on Robocrystallographer text descriptions, to predict crystal properties. It outperforms GNN baselines like ALIGNN on band gap and volume prediction while using fewer parameters.
LLM4Mol: ChatGPT Captions as Molecular Representations
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.
LMs Generate 3D Molecules from XYZ, CIF, PDB Files
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.
MolBERT: Auxiliary Tasks for Molecular BERT Models
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.