Review: Artificial Intelligence in Drug Discovery: Applications and Techniques
今天看了一篇综述，受益良多，对一些内容做一下总结
1. 摘要
人工智能(AI)在过去十年中一直在改变着药物发现(drug discovery)的实践。各种人工智能技术已被用于广泛的应用中，如虚拟筛选和药物设计。在这项调查中，我们首先对药物发现进行了概述，并讨论了相关的应用，这些应用可以简化为两个主要任务，即分子性质预测和分子生成。此外，为了总结人工智能在药物发现方面的进展，我们介绍了相关的人工智能技术，包括所调查论文中的模型架构和学习范式。我们希望这项调查能够为那些有兴趣在AI+药物发现领域的研究人员提供指导。我们还提供了一个GitHub资源库https://github.com/dengjianyuan/Survey_AI_Drug_Discovery，其中收集了论文和代码，作为学习资源，定期更新。
2. 关键概念定义
Table 1 关键概念定义
| Term | Description |
|---|---|
| Drug Discovery (药物发现) | 药物发现是在没有药物治疗某疾病或现有药物疗效有限(和/或)毒性严重的情况下进行的项目。 |
| leads | drug candidates |
| High-Throughput Screening (HTS) | a hit-finding approach underpinned by development in automation and the availability of large chemical libraries |
| SAR | structure-activity relationship |
| Virtual Screening (VS) | Various computational techniques have been developed to search the large chemical libraries for potentially active molecules to be tested in subsequent in vitro and in vivo assays. |
| structure-based VS | based on knowledge about the target -> increase the odds of identifying active molecules. |
| ligand-based VS | based on knowledge about known active ligands -> increase the odds of identifying active molecules. |
| agonist | 一种作为内源性配体激活靶点并发挥生物反应的分子 |
| antagonist | 结合靶标以抑制反应的分子。 |
| physicochemical property | water solubility, acid-base dissociation constant, lipophilicity, permeability… |
| pharmacokinetic property | absorption, distribution, metabolism, excretion |
| pharmacodynamic property | activity, selectivity |
| Synthetic Accessibility Score (SAS) | SAS is a heuristic score (10-1) of how hard or easy it is to synthesize a given molecule based on a combination of the molecular fragments’ contributions. |
| Quantitative Estimation of Drug-likeness (QED) | QED is an estimate (0-1) on how likely a molecule is a viable drug candidate. |
| quantitative structure-activity relationship (QSAR) | 对于每个感兴趣的属性，建立一个预测模型，通过分类或回归将分子结构映射到属性值。 |
| design-make-test-analysis (DMTA) cycle | 设计、制造、测试和分析周期 |
3. 数据, 表征, 基准
1. 公开数据资源
| Data Resources | Description | Link |
|---|---|---|
| PubChem | PubChem是全球最大的化学数据库，收集了750个数据源的化学信息。截至2020年8月，PubChem包含1.11亿个独特的化学结构，来自120万个生物分析实验的2.71亿个活性数据点。 | view |
| ChEMBL | 一个包含7700万SMILES字符串的精选数据集(from PubChem)。在ChEMBL22 (version 22)中，有超过160万种不同的化学结构，活性值超过1400万。 | view |
| ZINC | 用于目标预测的大规模基准数据集。由UCSF的Irwin和Shoichet实验室开发，包含分子，注释配体和靶标，以及超过1.2亿类药物化合物。 | view |
| PDBbind | PDBbind数据库的目的是提供储存在蛋白质数据库(PDB)中所有类型生物分子复合物的实验测量的结合亲和力数据的全面收集。它为这些复合物的能量信息和结构信息之间提供了必要的联系，有助于生物系统分子识别的各种计算和统计研究。 | view |
| BindingDB | BindingDB是一个公共的、可在网上访问的测量结合亲和力的数据库，主要关注被认为是药物靶点的蛋白质与小的、类药物分子的相互作用。截至2021年7月4日，BindingDB包含41,300个条目，每个条目都有一个DOI，包含8,547个蛋白质靶标和992,030个小分子的2,295,072个结合数据。 | view |
| DUD | 用于基准测试虚拟筛选。 | view |
| DUD-E | 增强和重建版本的DUD，DUD-E旨在通过提供具有挑战性的decoys来帮助基准分子对接计划 | view |
| MUV | 这些数据集提供了一个用于最大无偏验证 (MUV)的虚拟筛选方法的工具。 | view |
| STITCH | 化学-蛋白质相互作用网络 | view |
| GLL&GDD | The GDD, a GPCR Decoy Database, with its accompanying GPCR Ligand Library (GLL) have been compiled to help in GPCR docking. | view |
| NRLiSt BDB | 一个非商业性的人工管理基准数据库，专门用于核受体(Nuclear Receptor, NR)配体和结构药理学档案。 | view |
| KEGG | KEGG是一个从分子水平信息，特别是基因组测序等高通量实验技术生成的大规模分子数据集，了解细胞、生物、生态系统等生物系统的高级功能和用途的数据库 | view |
| DrugBank | 一个全面的免费在线数据库，包含有关药物和药物目标的信息。 | view |
| SIDER | SIDER包含已上市药品及其记录的药物不良反应的信息。信息是从公共文档和第三方库提取的。现有信息包括副作用频率、药物和副作用分类以及进一步信息的链接，例如药物靶标关系。 | view |
| OFFSIDES | 一个关于药物副作用的数据库，但并没有在FDA的官方标签上列出。是唯一全面的双方药效库。超过3300种药物和63000种组合与数百万种潜在的不良反应有关。 | view |
| TWO-SIDES | TWO-SIDES数据库是关于药物的多药副作用的资源。该数据库包含59,220对药物和1301个不良事件之间的868,221个显著关联。这些关联仅限于那些不能明确归因于任何一种药物单独的关联(即OFFSIDES所涵盖的关联)。根据比例报告比率(PRR)，该数据库还包含另外3782910个显著关联，其中药物对的副作用关联评分高于单独的药物。 | view |
| DILIrank | DILIrank DataSet是LTKB基准数据集的更新版本。DILIrank由1,036种FDA批准的药物组成，根据其导致药物诱导的肝损伤 (drug-induced liver injury, DILI)的可能性分为四类。DILI分类衍生自分析FDA批准的药物标签文件中提出的肝毒性描述，并评估文档中的因果关系。具体而言，这一最大的公开注释的DILI数据集包含三组 (Most-，Less- 和 No-DILI)，并将药物与肝损伤联系起来的证据，以及一个额外的分组 (Ambiguous-DILI-concern)，因果关系未确定。 | view |
2. 小分子表征
3. 基准平台
Table 2 基准平台总结
| Benchmark platforms | Description | Link |
|---|---|---|
| MoleculeNet | MoleculeNet: a benchmark for molecular machine learning (Chem Sci 2018) | paper code download |
| MolMapNet | Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations (Nat Mach Intell 2021) | paper code |
| ChemProp | Analyzing Learned Molecular Representations for Property Prediction (J Chem Inf Model 2019) | paper code website |
| REINVENT | Molecular De Novo design using Recurrent Neural Networks and Reinforcement Learning (J Cheminf 2017) | paper code |
| REINVENT 2.0 | an AI Tool for De Novo Drug Design (J Chem Inf Model 2020) | paper code |
| GraphINVENT | Graph Networks for Molecular Design (aka: GraphINVENT; ChemRxiv 2020) | paper code |
| GraphINVENT | Practical Notes on Building Molecular Graph Generative Models (Applied AI Letters 2020) | paper code |
| Guacamol | GuacaMol: Benchmarking Models for de Novo Molecular Design (J Chem Inf Model 2019) | paper code |
| MOSES | Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models (Front Pharmacol 2020) | paper code |
*. 评价指标
Table 3 分子性质预测和分子生成的评价指标
| Metric | Definition | Task Type |
|---|---|---|
| Accuracy | Correctly predictive rate | Classification |
| Recall | True positive rate | Classification |
| Precision | Positive predictive value | Classification |
| AUROC | Area under receiver-operating curve | Classification |
| AUPRC | Area under precision-recall curve | Classification |
| Recall@k | Recall among top-k retrieved molecules | Retrieval |
| Precision@k | Precision among top-k retrieved molecules | Retrieval |
| AP | Average Precision | Retrieval |
| MAE | Mean absolute error | Regression |
| RMSE | Rooted mean squared error | Regression |
| Validity | Fraction of valid molecules | Distribution learning |
| Uniqueness@k | Fraction of non-duplicates in k valid molecules | Distribution learning |
| Novelty | Fraction of molecules not shown in training set | Distribution learning |
| Diversity | Chemical diversity within generated molecules | Distribution learning |
| FCD | Fŕechet ChemNet Distance | Distribution learning |
| KL Divergence | Kullback-Leibler Divergence | Distribution learning |
| Scaffold Similarity | Similarity based on Bemis–Murcko scaffold | Goal-directed design |
| Rediscovery | Ability to re-discover target molecule | Goal-directed design |
4. 模型架构
1. 卷积神经网络 (CNN)
Task: Molecular Property Prediction; Representation*: images
| Name | Definition | Link |
| - | Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction (J Chem Inf Model 2017) | paper |
| Chemception | Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models (aka: Chemception; arXiv 2017) | paper |
| Toxic Colors | Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images (aka: Toxic Colors; J Chem Inf Model 2018) | paper |
| KekuleScope | KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images (J Cheminf 2019) | paper code |
| - | Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests (J Chem Inf Model 2019) | paper |
| DEEPScreen | DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representation (Chem Sci 2020) | paper code |
Task: Molecular Property Prediction; Representation*: fingerprint
| Name | Definition | Link |
|---|---|---|
| - | Massively Multitask Networks for Drug Discovery (arXiv 2015) | paper |
| - | Convolutional Networks on Graphs for Learning Molecular Fingerprints (NeurIPS 2015) | paper code |
Task: Molecular Structure Extraction and Recognition
| Name | Definition | Link |
|---|---|---|
| MSE-DUDL | Molecular Structure Extraction from Documents Using Deep Learning (J Chem Inf Model 2019) | paper |
| DECIMER-Segmentation | DECIMER-Segmentation: Automated extraction of chemical structure depictions from scientific literature (J Cheminf 2021) | paper |
| DECIMER | DECIMER: towards deep learning for chemical image recognition (J Cheminf 2020) | paper code |
| DECIMER 1.0 | DECIMER 1.0: Deep Learning for Chemical Image Recognition using Transformers (chemRxiv 2021) | paper |
Task: Molecular Property Prediction; Representation*: SMILES Strings
| Name | Definition | Link |
|---|---|---|
| SMILES2Vec | SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties (aka: SMILES2Vec; arXiv 2017) | paper |
| - | Large-scale comparison of machine learning methods for drug target prediction on ChEMBL (aka:SmilesLSTM; Chem Sci 2018) | paper code |
Task: Molecule Generation; Representation*: SMILES Strings
| Name | Definition | Link |
|---|---|---|
| - | Molecular de‑novo design through deep reinforcement learning (aka: REINVENT; J Cheminf 2017) | paper |
| - | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks (aka: CharRNN; ACS Cent Sci 2018) | paper |
| - | Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design (ICLR 2018 Workshop) | paper |
| ReLeaSE | Deep Reinforcement Learning for de novo Drug Design（aka: ReLeaSE; Sci Adv 2018） | paper code |
| DeepFMPO | Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design (J Chem Inf Model 2019) | paper code |
Task: Molecule Generation; Representation*: Molecular Graphs
| Name | Definition | Link |
|---|---|---|
| GraphRNN | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models (aka: GraphRNN; ICML 2018) | paper code |
| - | Learning Deep Generative Models of Graphs (ICML 2018) | paper code |
| MolecularRNN | MolecularRNN: Generating realistic molecular graphs with optimized properties (arXiv 2019) | paper |
3. 图神经网络 (GNN)
Task: Molecular Property Prediction; Representation*: Molecular Graphs
| Work | Link |
|---|---|
| Molecular Graph Convolutions: Moving Beyond Fingerprints (aka: Weave; J Comput Aided Mol Des 2016) | paper |
| Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction (J Chem Inf Model 2017) | paper |
| Semi-supervised classification with graph convolutional networks (aka: GraphConv; ICLR 2017) | paper code |
| Neural Message Passing for Quantum Chemistry (aka: MPNN; ICML 2017) | paper code |
| SchNet: A continuous-filter convolutional neural network for modeling quantum interactions (aka: SchNet; NeurIPS 2017) | paper code |
| Low Data Drug Discovery with One-Shot Learning (ACS Cent Sci 2017) | paper |
| Large-scale comparison of machine learning methods for drug target prediction on ChEMBL (aka:SmilesLSTM; Chem Sci 2018) | paper code |
| PotentialNet for Molecular Property Prediction (aka: PotentialNet; ACS Cent Sci 2018) | paper |
| Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective (aka: MGCN; AAAI 2019) | paper |
| Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity (J Chem Inf Model 2019) | paper code |
| DeepChemStable: Chemical Stability Prediction with an Attention-Based Graph Convolution Network (J Chem Inf Model 2019) | paper |
| Analyzing Learned Molecular Representations for Property Prediction (aka: Chemrop, D-MPNN; J Chem Inf Model 2019) | paper code |
| Molecule Property Prediction Based on Spatial Graph Embedding (aka: C-SGEN; J Chem Inf Model 2019) | paper code |
| Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism (aka: Attentive FP; J Med Chem 2019) | paper code |
| Graph convolutional neural networks as” general-purpose” property predictors: the universality and limits of applicability (J Chem Inf Model 2020) | paper |
| N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules (aka: N-Gram Graph; NeurIPS 2019) | paper |
| Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction (J Cheminf 2020) | paper code |
| A self‑attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility (J Cheminf 2020) | paper code |
| Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-Like Molecules (aka: CIGIN; AAAI 2020) | paper code |
| Strategies for Pre-training Graph Neural Networks (ICLR 2020) | paper code |
| Directional Message Passing for Molecular Graphs (aka: DimeNet; ICLR 2020) | paper code |
| Drug–target affinity prediction using graph neural network and contact maps (RSC Advances 2020) | paper |
| ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction (aka: ASGN; KDD 2020) | paper code |
| Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction (ICML 2020 Workshop) | paper code |
Task: Molecule Generation; Representation*: Molecular Graphs
| Work | Link |
|---|---|
| Multi‑objective de novo drug design with conditional graph generative model (J Cheminf 2018) | paper code |
| Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (aka: GCPN; NeurIPS 2018) | paper code |
| Optimization of Molecules via Deep Reinforcement Learning (aka: MolDQN; Sci Rep 2019) | paper |
| Improving Molecular Design by Stochastic Iterative Target Augmentation (ICML 2020) | paper code |
| DeepGraphMolGen, a multi‑objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach (J Cheminf 2020) | paper |
| Reinforced Molecular Optimization with Neighborhood-Controlled Grammars (aka: MNCE-RL; NeurIPS 2020) | paper code |
| Graph Networks for Molecular Design (aka: GraphINVENT; ChemRxiv 2020) | paper code |
Common GNN Models
| Work | Link |
|---|---|
| Recurrent GNNs Gated graph sequence neural networks (aka: GGNN; ICLR 2016) | paper code |
| Convolutional GNNs (Spectral-based) Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (aka: ChebNet; NeurIPS 2016) | paper code |
| Convolutional GNNs (Spectral-based) Semi-supervised classification with graph convolutional networks (aka: GraphConv; ICLR 2017) | paper code |
| Convolutional GNNs (Spatial-based) Neural message passing for quantum chemistry (aka: MPNN; ICML 2017) | paper code |
| Convolutional GNNs (Spatial-based) Inductive Representation Learning on Large Graphs (aka: GraphSAGE; NeurIPS 2017) | paper code |
| Convolutional GNNs (Spatial-based) Graph Attention Networks (aka: GAT; ICLR 2018) | paper code |
| Convolutional GNNs (Spatial-based) How powerful are graph neural networks? (aka: GIN; ICLR 2019) | paper code |
4. 变分编码器 (VAE)
Task: Molecule Generation; Representation*: SMILES Strings
| Work | Link |
|---|---|
| Automatic chemical design using a data-driven continuous representation of molecules (arXiv 2016; ACS Cent Sci 2018) | paper code |
| Grammar Variational Autoencoder (aka: GrammarVAE; ICML 2017) | paper |
| Application of Generative Autoencoder in De Novo Molecular Design (Mol Inform 2017) | paper |
| Syntax-Directed Variational Autoencoder for Structured Data (aka: SD-VAE; ICLR 2018) | paper code |
| Conditional Molecular Design with Deep Generative Models (aka: Continuous SSVAE; J Chem Inf Model 2018) | paper code |
| Molecular generative model based on conditional variational autoencoder for de novo molecular design (aka: CVAE; J Cheminf 2018) | paper code |
| Constrained Graph Variational Autoencoders for Molecule Design (aka: CGVAE; NeurIPS 2018) | paper code |
| NEVAE: A Deep Generative Model for Molecular Graphs (aka: NeVAE; AAAI 2019) | paper code |
| De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping (aka: GTMVAE; J Chem Inf Model 2019) | paper |
| Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation (aka: re-balanced VAE; ACM BCB 2020) | paper code |
| CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models (aka: CogMol; NeurIPS 2020) | paper code |
VAE变体: AAE
| Work | Link |
|---|---|
| Application of Generative Autoencoder in De Novo Molecular Design (Mol Inform 2017) | paper |
| druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico (aka: druGAN; Mol Pharm 2017) | paper |
| Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery (aka: SAAE; Mol Pharm 2018) | paper |
Task*: Molecule Generation; Representation*: Molecular Graphs
| Work | Link |
|---|---|
| GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders (aka: GraphVAE; arXiv 2018) | paper |
| Junction Tree Variational Autoencoder for Molecular Graph Generation (aka: JT-VAE; ICML 2018) | paper code |
| Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders (aka:Regularized VAE; NeurIPS 2018) | paper |
| Molecular Hypergraph Grammar with Its Application to Molecular Optimization (aka: MHG-VAE; ICML 2019) | paper code |
| Efficient learning of non‑autoregressive graph variational autoencoders for molecular graph generation (J Cheminf 2019) | paper code |
| Deep learning enables rapid identification of potent DDR1 kinase inhibitors (aka: GENTRL; Nat Biotechnol 2019) | paper code |
| Scaffold-based molecular design using graph generative model (aka: ScaffoldVAE; arXiv 2019) | paper |
| Learning Multimodal Graph-to-Graph Translation for Molecule Optimization (aka: VJTNN; ICLR 2019) | paper code |
| CORE: Automatic Molecule Optimization Using Copy & Refine Strategy (AAAI 2020) | paper code |
| Hierarchical Generation of Molecular Graphs using Structural Motifs (aka: HierVAE; ICML 2020) | paper code |
| Compressed graph representation for scalable molecular graph generation (J Cheminf 2020) | paper code |
Reaction & Retrosynthesis Prediction; Representation*: Molecular Graphs
| Work | Link |
|---|---|
| Generating Molecules via Chemical Reactions (ICLR 2019 Workshop) | paper |
| Barking up the right tree: an approach to search over molecule synthesis DAG (NeurIPS 2020) | paper code |
| paper |
5. 对抗生成网络 (GAN)
Task: Molecule Generation; Representation*: SMILES Strings
| Work | Link |
|---|---|
| Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models (aka: ORGAN; ArXiv 2017) | paper code |
| Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (aka: ORGANIC; ChemRxiv 2017) | paper code |
| Reinforced Adversarial Neural Computer for de Novo Molecular Design (aka: RANC; J Chem Inf Model 2018) | paper |
Molecule Generation; Representation*: Molecular Graphs
| Work | Link |
|---|---|
| MolGAN: An implicit generative model for small molecular graphs (aka: MolGAN; ICML 2018 Workshop) | paper code-tensorflow code-pytorch |
6. Normalizing Flow Models
Task*: Molecule Generation; Representation*: Molecular Graphs
| Work | Link |
|---|---|
| GraphNVP: An Invertible Flow Model for Generating Molecular Graphs (aka: GraphNVP; arXiv 2019) | paper code |
| Graph Residual Flow for Molecular Graph Generation (aka: GRF; arXiv 2019) | paper |
| GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation (aka: GraphAF; ICLR 2020) | paper code |
| MoFlow: An Invertible Flow Model for Generating Molecular Graphs (aka: MoFlow; KDD 2020) | paper code |
| GraphDF: A Discrete Flow Model for Molecular Graph Generation (aka: GraphDF; ICML 2021) | paper |
7. Transformers
Task*: Molecular Property Prediction; Representation*: SMILES Strings
| Work | Link |
|---|---|
| SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction (aka: SMILES-BERT; ACM BCB 2019) | paper |
| SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery (aka: SMILES Transformer; arXiv 2019) | paper code |
| ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction (aka: ChemBERTa; arXiv 2020) | paper code |
| Molecular representation learning with language models and domain-relevant auxiliary tasks (aka: MolBERT; NeurIPS 2020 Workshop) | paper code |
Task*: Molecular Property Prediction; Representation*: Molecular Graphs
| Work | Link |
|---|---|
| Self-Supervised Graph Transformer on Large-Scale Molecular Data (aka: GROVER; NeurIPS 2020) | paper |
Task*: Molecule Generation; Representation*: Molecular Graphs
| Work | Link |
|---|---|
| A Model to Search for Synthesizable Molecules (aka: Molecule Chef; NeurIPS 2019) | paper code |
| Transformer neural network for protein-specific de novo drug generation as a machine translation problem (Sci Rep 2021) | paper |
5. 学习范式
1. 分子性质预测中的自监督学习
Generative Learning
| Work | Link |
|---|---|
| Strategies for Pre-training Graph Neural Networks (ICLR 2020) | paper code |
| Molecular representation learning with language models and domain-relevant auxiliary tasks (aka: MolBERT; NeurIPS 2020 Workshop) | paper code |
| Self-Supervised Graph Transformer on Large-Scale Molecular Data (aka: GROVER; NeurIPS 2020) | paper |
Contrastive Learning
| Work | Link |
|---|---|
| MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks (ArXiv 2021) | paper |
2. 分子生成中的强化学习
Reinforcement Learning in Molecule Generation
| Work | Link |
|---|---|
| Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models (aka: ORGAN; ArXiv 2017) | paper code |
| Molecular de‑novo design through deep reinforcement learning (aka: REINVENT; J Cheminf 2017) | paper |
| Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (aka: ORGANIC; ChemRxiv 2017) | paper code |
| Reinforced Adversarial Neural Computer for de Novo Molecular Design (aka: RANC; J Chem Inf Model 2018) | paper |
| Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design (ICLR 2018 Workshop) | paper |
| MolGAN: An implicit generative model for small molecular graphs (aka: MolGAN; ICML 2018 Workshop) | paper code-tensorflow code-pytorch |
| Deep Reinforcement Learning for de novo Drug Design（aka: ReLeaSE; Sci Adv 2018) | paper code |
| Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (aka: GCPN; NeurIPS 2018) | paper code |
| Deep learning enables rapid identification of potent DDR1 kinase inhibitors (aka: GENTRL; Nat Biotechnol 2019) | paper code |
| Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design (aka: DeepFMPO; J Chem Inf Model 2019) | paper code |
| Optimization of Molecules via Deep Reinforcement Learning (aka: MolDQN; Sci Rep 2019) | paper |
| Efficient learning of non‑autoregressive graph variational autoencoders for molecular graph generation (J Cheminf 2019) | paper code |
| GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation (aka: GraphAF; ICLR 2020) | paper code |
| Reinforcement Learning for Molecular Design Guided by Quantum Mechanics (cka: MolGym; ICML 2020) | paper |
| DeepGraphMolGen, a multi‑objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach (aka: DeepGraphMolGen; J Cheminf 2020) | paper |
| Reinforced Molecular Optimization with Neighborhood-Controlled Grammars (aka: MNCE-RL; NeurIPS 2020) | paper code |
Common RL Algorithms
| Work | Link |
|---|---|
| Value-based Playing Atari with Deep Reinforcement Learning (aka: DQN; NeurIPS Workshop 2013) | paper |
| Value-based Human-level control through deep reinforcement learning (aka: DQN; Nature 2015) | paper |
| Value-based Deep Reinforcement Learning with Double Q-learning (aka: Double Q-learning; AAAI 2016) | paper |
| Value-based Prioritized Experience Replay (aka: DQN with Experience Replay; ICLR 2016) | paper |
| Value-based Dueling Network Architectures for Deep Reinforcement Learning (aka: Dueling Network; ICML 2016) | paper |
| Policy-gradient Simple statistical gradient-following algorithms for connectionist reinforcement learning (aka: REINFORCE; Mach Learn 1992) | paper |
| Policy-gradient Policy Gradient Methods for Reinforcement Learning with Function Approximation (aka: Random Policy Gradient; NeurIPS 1999) | paper |
| Policy-gradient Deterministic Policy Gradient Algorithms (aka: DPG; ICML 2014) | paper |
| Policy-gradient Trust Region Policy Optimization (aka: TRPO; ICML 2015) | paper |
| Policy-gradient Proximal Policy Optimization Algorithms (aka: PPO; arXiv 2017 2015) | paper |
| Hybrid Continuous control with deep reinforcement learning (aka: DDPG; ICLR 2016) | paper |
| Hybrid Asynchronous Methods for Deep Reinforcement Learning (aka: A3C; ICML 2016) | paper |
Pareto Optimality (帕累托最优)
| Work | Link |
|---|---|
| De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization (J Chem Inf Model 2020) | paper code |
| Multiobjective de novo drug design with recurrent neural networks and nondominated sorting (J Cheminf 2020) | paper |
| DrugEx v2: De Novo Design of Drug Molecule by Pareto-based Multi-Objective Reinforcement Learning in Polypharmacology (ChemRxiv) | paper |
| paper |
Reaction & Retrosynthesis Optimization
| Work | Link |
|---|---|
| Optimizing chemical reactions with deep reinforcement learning (ACS Cent Sci 2017) | paper |
3. Other
Metric Learning
| Work | Link |
|---|---|
| Machine-guided representation for accurate graph-based molecular machine learning (Phys Chem Chem Phys 2020) | paper |
| Embedding of Molecular Structure Using Molecular Hypergraph Variational Autoencoder with Metric Learning (Mol Inform 2020) | paper |
Few-Shot Learning
| Work | Link |
|---|---|
| Low Data Drug Discovery with One-Shot Learning (ACS Cent Sci 2017) | paper |
| paper |
Meta Learning
| Work | Link |
|---|---|
| Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction (ICML 2020 Workshop) | paper code |
Active Learning
| Work | Link |
|---|---|
| ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction (aka: ASGN; KDD 2020) | paper code |
| Evidential Deep Learning for Guided Molecular Property Prediction and Discovery (NeurIPS 2020 Workshop) | paper |
6. 解决现有挑战
1. Model Interpretation
| Work | Link |
|---|---|
| Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome−Inhibitor Interaction Landscapes (J Chem Inf Model 2018) | paper |
| Using attribution to decode binding mechanism in neural network models for chemistry (PNAS 2019) | paper |
| Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It? (J Chem Inf Model 2019) | paper |
| Building of Robust and Interpretable QSAR Classification Models by Means of the Rivality Index (J Chem Inf Model 2019) | paper |
2. Dataset Concerns
| Work | Link |
|---|---|
| In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening (J Chem Inf Model 2019) | paper |
| Deep Learning-Based Imbalanced Data Classification for Drug Discovery (J Chem Inf Model 2020) | paper code |
3. Uncertainty Estimation
| Work | Link |
|---|---|
| General Approach to Estimate Error Bars for Quantitative Structure−Activity Relationship Predictions of Molecular Activity (J Chem Inf Model 2018) | paper |
| Assessment and Reproducibility of Quantitative Structure−Activity Relationship Models by the Nonexpert (J Chem Inf Model 2018) | paper |
| Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Prediction Errors for Deep Neural Networks (J Chem Inf Model 2018) | paper |
| Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout (J Chem Inf Model 2019) | paper |
| Minimal-uncertainty prediction of general drug-likeness based on Bayesian neural networks (Nat Mach Intell 2020) | paper |
| Assigning Confidence to Molecular Property Prediction (arXiv 2021) | paper |
| Gi and Pal Scores: Deep Neural Network Generalization Statistics (ICLR 2021 Workshop) | paper |
4. Representation Capacity
| Work | Link |
|---|---|
| Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure (J Chem Inf Model 2019) | paper |
| Optimal Transport Graph Neural Networks (arXiv 2020) | paper |
5. Out-of-Distribution Generalization
| Work | Link |
|---|---|
| Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure−Activity Relationship Models Based on Deep Neural Networks? (J Chem Inf Model 2018) | paper |
| Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization (J Chem Inf Model 2018) | paper |
| Molecular Similarity-Based Domain Applicability Metric Efficiently Identifies Out-of-Domain Compounds (J Chem Inf Model 2018) | paper |
7. 参考文献
- Deng, Jianyuan & Yang, Zhibo & Ojima, Iwao & Samaras, Dimitris & Wang, Fusheng. (2021). Artificial Intelligence in Drug Discovery: Applications and Techniques.
- Post title：Review: Artificial Intelligence in Drug Discovery: Applications and Techniques
- Post author：Jiacai-Yi
- Create time：2021-07-21 22:31
- Post link：https://blog.iamkotori.com/2021/07/21/AIDD-A-and-T/
- Copyright Notice：All articles in this blog are licensed under BY-NC-SA unless stating additionally.
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