ML+AI

• MolPipeline: MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn
  
• pQSAR: build massively multitask, two-step machine learning models with unprecedented scope, accuracy, and applicability domain
  
• ZairaChem: Automated ML-based (Q)SAR
  

AI-Augmented R-Group Exploration

• AIR: AI-Augmented R-Group Exploration in Medicinal Chemistry
  

CNN

• conv_qsar_fast: QSAR|QSPR using descriptor-free molecular embedding
  

Complete Package

• DeepPurpose: A Deep Learning Library for Compound and Protein Modeling, DTI, Drug Property, PPI, DDI, Protein Function Prediction
  
• Oloren ChemEngine: unified API for the development and use of molecular property predictors
  
• SPOC: A tool for calculating spatial and physicochemical descriptors from molecular dynamics simulations.
  

DNN

• DeepNeuralNet-QSAR: For training a multi-task DNN with dense QSAR dataset(s)
  

Ensemble

• AIS-Ensemble: Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction
  

Few-Shot

• FewGS: This repository contains source code and datasets for "Few-Shot Graph and SMILES Learning for Molecular Property Prediction."
  
• Few-Shot-Learning-for-Low-Data-Drug-Discovery: Low Data Drug Discovery with One-Shot Learning
  
• FS-Mol: A Few-Shot Learning Dataset of Molecules
  
• Meta-MGNN:
  
• MHNfs: Context-enriched molecule representations improve few-shot drug discovery, available on HuggingFace
  
• MolecularGPT: MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
  
• MolFesCue: molecular property prediction in Data-Limited and imbalanced contexts using Few-Shot and contrastive learning
  
• PG-DERN: Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
  

GAT

• LGGA:
  

GNN

• CheMixNet: A mixed DNN architecture that predicts chemical properties using multiple molecular representations.
  
• DeepDelta: A pairwise deep learning approach predicting property differences between two molecules.
  
• MMGX: Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX
  
• MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks
  
• MolE: Molecular representations through redundancy reduction of Embeddings
  
• molgraph: Offers graph neural networks with TensorFlow and Keras for molecular machine learning, focusing on compatibility and ease of use.
  
• PA-GNN: Graph Neural Network-Based Molecular Property Prediction with Patch Aggregation
  

Graph-Fusion

• MolProp: MolPROP: Molecular Property prediction with multimodal language and graph fusion
  

Hybrid

• FGNN: A New Fingerprint and Graph Hybrid Neural Network for Predicting Molecular Properties
  

LLM

• ChemFM: A Foundation Model for Chemical Design and Property Prediction
  
• MolecularGPT: MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
  
• MolPMoFiT: transfer learning method based on self-supervised pre-training + task-specific fine-tuning for QSPR/QSAR modeling
  

MPGNN

• Graphormer: deep learning package that allows researchers and developers to train custom models for molecule modeling tasks
  

Other NN

• ChemProp: Features a deep learning approach for molecular property prediction, focusing on scalability and fast uncertainty quantification.
  
• GeminiMol: Incorporates conformational space profile into molecular representation learning, enhancing drug discovery including virtual screening, target identification, and QSAR.
  

Pretrained Models

• OPERA: Open-source QSAR models for pKa prediction using multiple machine learning approaches. Also suite of QSAR models (windows, linux), recent implementation (CATMoS Acute Toxicity Modeling Suite, acute oral toxicity) (standalone).
  

Reviews

• A Survey of Graph Neural Network Architectures in Ligand Binding Affinity Prediction Models: A Survey of Graph Neural Network Architectures in Ligand Binding Affinity Prediction Models
  

Several architectures

• DeltaClassifiers: a novel molecular pairing approach to process this data. This creates a new classification task of predicting which one of two paired molecules is more potent.
  

Transfer Learning

• PGM: Fast and effective molecular property prediction with transferability map
  

Transformer

• ChemBERTa: BERT-like models applied to chemical SMILES data for drug design, chemical modelling, and property prediction
  
• GPT-MolBERTa: A text-based molecular property prediction model utilizing a novel approach to represent SMILES molecules.
  
• KnoMol: KnoMol: A Knowledge-Enhanced Graph Transformer for Molecular Property Prediction
  
• PointGAT:
  
• TOML-BERT:
  
• X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis