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List of archived papers/tools

This page contains a list of papers/tools published before 2023 and that have been added to the main readme of this repo in the past.

Molecules

  • MolDQN - Optimization of molecules via Deep Reinforcement Learning. [Paper]
  • Pasithea - Deep Molecular Dreaming: Inverse Machine Learning for de-novo molecular design and interpretability with surjective representations. [Paper]
  • fragment-based-dgm - A Deep Generative Model for fragment-based molecule generation. [Paper]
  • MAT - Molecule Attention Transformer for molecular prediction tasks. [Paper].
  • GLAMOUR - Chemistry-informed Macromolecule Graph Representation for Similarity Computation, Unsupervised and Supervised Learning. [Paper]
  • Transformer-M - One Transformer that can understand both 2D & 3D molecular data. [Paper]
  • SynNet - An amortized approach to synthetic tree generation using neural networks. This model can serve as both a synthesis planning tool and as a tool for synthesizable molecular design. [Paper]
  • SPIB - SPIB (State Predictive Information Bottleneck) is a Deep Learning-based framework that learns the reaction coordinates from high dimensional molecular simulation trajectories. [Paper]
  • MolT5 - A self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings. [Paper]
  • DIONYSUS - An extensive study of the calibration and generalizability of probabilistic Machine Learning models on small chemical datasets. [Paper]
  • NVIDIA-PCQM4Mv2 - Heterogenous ensemble of models for Molecular Property Prediction. [Paper]
  • JAEGER - JT-VAE Generative Modeling (JAEGER) is a deep generative approach for small-molecule design. It is based on the Junction-Tree Variational Auto-Encoder (JT-VAE) method. [JT-VAE paper]
  • MoLFormer - A large-scale chemical language model designed with the intention of learning a model trained on small molecules which are represented as SMILES strings. [Paper]
  • Mol-CycleGAN - A generative model for molecular optimization. [Paper]
  • EDM - Equivariant Diffusion for Molecule Generation in 3D. [Paper]

Proteins

  • Meaningful Protein Representation - Learning meaningful representations of protein sequences using a VAE. [Article] [Paper].
  • TAPE - Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. [Paper]
  • Protein Sequence Embedding - Learning protein sequence embeddings using information from structure. [Paper]
  • IdpGAN - A GAN to generate different 3D conformations for intrinsically disordered proteins given their sequences. [Article]
  • PocketMiner - A tool for predicting the locations of cryptic pockets from single protein structures. [Paper]
  • progen2 - A suite of open-sourced projects and models for protein engineering and design. [Paper]
  • TransformerCPI - Improving compound–protein interaction prediction by sequence-based Deep Learning with self-attention mechanism and label reversal experiments. [Paper]
  • EvoBind - In silico directed evolution of peptide binders with AlphaFold2. [Paper]
  • ProtGPT2 - A deep unsupervised language model for protein design. [Article]
  • Bio Embeddings - General purpose Python embedders based on open models trained on biological sequence representations. [Paper]
  • ProteinMPNN - Robust Deep Learning based protein sequence design. [Paper]
  • MoLPC - Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search. [Article]
  • foldingdiff - A diffusion model for generating novel protein backbone structures. [Paper]
  • ProGen - Suite of open-sourced projects and models for protein engineering and design. [Paper]
  • DeepAb - Antibody structure prediction using interpretable Deep Learning. [Paper]
  • cdna-display-proteolysis-pipeline - Mega-scale experimental analysis of protein folding stability in biology and protein design. [Paper]
  • GearNet - Geometric pretraining methods for Protein Structure Representation Learning. [Paper]
  • RFdiffusion - Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models. [Paper]
  • ProteinGLUE - A multi-task benchmark suite for self-supervised protein modeling. [Paper]