New Chemical Design/Property Prediction/Target DeConvoltion

List of papers

category Paper Model Takeaway
New Chemical Design BenovolentAI RNN based RL (HC-MLE) Reinfocement Learning with 19 benchmark
  VAE_property VAE(1D CNN & RNN) wth property(GP) Variatioanl AutoEncoder jointly predicting property
  ChemTS MonteCarlo Tree Search with RNN Cascading way and RNN is used at RollOut step
  DruGAN Adversarial AutoEncoder AAE is better than VAE in terms of Reconstruction error and Diversity
  InSilico RNN-LSTM Unique Scaffolds can be achieved
Property Prediction DeepChem Graph Convolution Considering 2D strucutre is critical
Target DeConvoltion SwissTarget Logist Regression 3D similarity based on ligands

To be compact, I didn’t upload Input, related property, and data resources though being created. If somebody pursue for more details about each paper, please feel free to email me.


Algorithms

For the sake of compactness, I would demonstrate two methods Hillclimb MLE (HC-MLE) in Reinfocement Learning and Monte Carlo Tree Search from BenovolentAI and ChemTS papers. The second paper, VAE with Property, is reviewed in my previous post.


1. Hillclimb MLE (HC-MLE)

beno_mle_algorithm

First, There are 19 benchmarks that used for Reward in Reinforcement Learning. They can be catagorized into Validity, Diversity, Physio-Chemical Property, similarity with 10 representative compounds, Rule of 5, and MPO. Secondly, HC-MLE mazimizes the likelihood of sequences that received Top K highest reward.


2. Monte Carlo Tree Search (MCTS)

chemts_procedure

  1. Pretrain RNN and Get Conditional Probability
  2. Conditional Probability is used in MCTS as sampling distribution to get next character and elongate the smiles code
  3. Reward score of generated smiles code is computed
  4. In Back propagation, reward is back propagated & UCB at each node is updated


Reference

  • benevolent (EXPLORING DEEP RECURRENT MODELS WITH REINFORCEMENT LEARNING FOR MOLECULE DESIGN): benevolent
  • VAE with Property (Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules): vae_property
  • ChemTS (Python Library): ChemTS
  • druGAN (druGAN: An Advanced Generative Adversarial Autoencoder Model 2 for de Novo Generation of New Molecules with Desired Molecular 3 Properties in Silico ): druGAN
  • InSilico (In silico generation of novel, drug-like chemical matter using the LSTM neural network ): InSilico

  • DeepChem (Applying Automated Machine Learning to Drug Discovery) : N/A

  • SwissTarget (SwissTargetPrediction: a web server for target prediction of bioactive small molecules ): SwissTarget