An evolving trajectory in drug design

The purpose of this note is to act as a repository for all the literature read during my PhD. The literature read has been segregated in different domains ranging from dynamics to representing molecule as a 3d object.

- Paper Count = 77 /1280
+ Reading = 5

• Dynamics

  • ODE²VAE
  • Neural ODEs
  • Neural Flows
  • FFJORD
  • Neural Processes
  • Review of Normalizing flows
  • Variational multiple shooting for Bayesian ODEs with Gaussian processes
  • Non-Autoregressive neural machine translation
  • Your classifier is secretly an energy based model and you should treat it like one
  • Neural Relational Inference for Interacting systems
  • Learning Continuous-time PDEs from sparse data with GNNs
  • PointFlow
  • Conditional Random fields
  • Message Passing Neural PDE Solvers
  • Neural Controlled Differential Equations
  • Variational Neural Cellular Automata
  • Learning to generate 3D shapes with generative cellular automata
  • Neural process with stochastic attention

• Graph based learning

  • Graph Condensation for Graph Neural Networks
  • Simple GNN Regularisation for 3D Molecule Property prediction
  • Graph Normalizing flows
  • Evolving-Graph Gaussian Processes
  • Handling distribution shifts in graphs
  • Learning to Solve PDE-constrained Inverse Problems with Graph Networks
  • Topological graph neural networks
  • PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
  • Spherical Message passing for 3D Graph Networks
  • Temporal Graph Networks
  • E(n) Equivariant Graph Neural Networks
  • Graph Coupled Oscillator Networks

• Physics Inspired ML

  • Noether Networks
  • Interaction Networks for Learning about Objects,Relations and Physics
  • Physics Informed Machine Learning
  • Deconstructing the inductive biases of Hamiltonian neural networks
  • Hamiltonian graph networks with ODE integrators
  • Predicting physics in mesh-reduced space with temporal attention

• Conformer generation and structure prediction

  • Learning Gradient Fields for Molecular Conformation Generation
  • DGSM
  • Learning neural generative dynamics for molecular conformation generation
  • Geometric Deep Learning on Molecular Representations
  • EQUIBIND: Geometric Deep Learning for Drug Binding Structure Prediction
  • Spanning Tree-based Graph Generation for Molecules
  • Molecular Surface Representation Using 3D Zernike Descriptors for Protein Shape Comparison and Docking
  • Categorical Normalizing Flow
  • GraphDF: A Discrete Flow Model for Molecular Graph Generation
  • GraphNVP
  • GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
  • Dual use of artifcial-intelligence-powered drug discovery
  • Energy-Inspired Molecular Conformation Optimization
  • Chemical-Reaction-Aware Molecule Representation Learning
  • Evaluating generalization in Gflow Nets for molecule design
  • An auto regressive flow model for 3D molecular geometry generation from scratch
  • Molecular RNN
  • Learning to extend molecular scaffolds with structural motifs
  • Learning 3D representations of molecular chirality with invariance to bond rotations
  • Pre-training molecular graph representation with 3D geometry
  • DATA-EFFICIENT GRAPH GRAMMAR LEARNING FOR MOLECULAR GENERATION
  • Geometric Transformers for protein interface contact prediction
  • A 3D Molecule Generative Model for Structure-Based Drug Design
  • Crystal Diffusion Variational Autoencoder for Periodic Material Generation
  • Generative Coarse-Graining of Molecular Conformations
  • Generating 3D Molecules for Target Protein Binding

• Diffusion, SDE and Score Matching Methods

  • GeoDiff
  • Diffusion Kernels on Graphs and Other Discrete Input Spaces
  • Denoising Probabilistic Diffusion models
  • Estimation of Non-Normalized Statistical Models by Score Matching
  • GRAND: Graph Neural Diffusion
  • Learning energy-based models by diffusion recovery likelihood
  • Denoising Diffusion GANs
  • Scalable Gradients for SDE
  • Graph Anisotropic Diffusion
  • Auto-Regressive Diffusion Models
  • GRAND++
  • Reimaninan Neural SDE
  • Neural Sheaf Diffusion
  • Neural SDEs as Infinite-Dimensional GANs
  • Score-based generative modeling through SDE

• RL

  • Reinforcement Learning and Control as Probabilistic Inference