1
Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule
2
On the Frustration to Predict Binding Affinities from Protein-Ligand Structures with Deep Neural Networks.
3
Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design
4
AI and the Everything in the Whole Wide World Benchmark
5
Maxsmi: maximizing molecular property prediction performance with confidence estimation using SMILES augmentation and deep learning
6
Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models
7
Benchmarking Molecular Feature Attribution Methods with Activity Cliffs
9
Geometric deep learning on molecular representations
10
Highly accurate protein structure prediction with AlphaFold
11
Accurate prediction of protein structures and interactions using a 3-track neural network
12
Nonadditivity in public and inhouse data: implications for drug design
13
Predicting molecular activity on nuclear receptors by multitask neural networks
14
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
15
Pushing the boundaries of molecular representation for drug discovery with graph attention mechanism.
16
Transfer Learning for Drug Discovery.
17
Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery
18
Advances in exploring activity cliffs
19
Open Graph Benchmark: Datasets for Machine Learning on Graphs
20
A Deep Learning Approach to Antibiotic Discovery
21
Experimental error, kurtosis, activity cliffs, and methodology: What limits the predictivity of QSAR models?
22
Introducing a new category of activity cliffs combining different compound similarity criteria.
23
Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events.
24
PyTorch: An Imperative Style, High-Performance Deep Learning Library
25
4D- quantitative structure–activity relationship modeling: making a comeback
26
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.
27
Synthetic organic chemistry driven by artificial intelligence
28
SciPy 1.0: fundamental algorithms for scientific computing in Python
29
Randomized SMILES strings improve the quality of molecular generative models
30
Introducing a new category of activity cliffs with chemical modifications at multiple sites and rationalizing contributions of individual substitutions.
31
Strategies for Pre-training Graph Neural Networks
32
Analyzing Learned Molecular Representations for Property Prediction
33
Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening
34
In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening
35
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
36
GuacaMol: Benchmarking Models for De Novo Molecular Design
37
Scaffold-Hopping from Synthetic Drugs by Holistic Molecular Representation
38
Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
39
Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks
40
PubChem 2019 update: improved access to chemical data
41
How Powerful are Graph Neural Networks?
43
On the Misleading Use of QF32
for QSAR Model Comparison
44
The rise of deep learning in drug discovery.
45
Machine Learning in Computer-Aided Synthesis Planning.
46
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
47
Progress with modeling activity landscapes in drug discovery
48
PotentialNet for Molecular Property Prediction
49
De Novo Design of Bioactive Small Molecules by Artificial Intelligence
50
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
51
Generative Recurrent Networks for De Novo Drug Design
52
Graph Attention Networks
53
Planning chemical syntheses with deep neural networks and symbolic AI
54
Representation and identification of activity cliffs
55
Inductive Representation Learning on Large Graphs
56
Neural Message Passing for Quantum Chemistry
57
Chemical Space Mimicry for Drug Discovery
58
SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
59
MoleculeNet: a benchmark for molecular machine learning
60
Computational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches
61
Semi-Supervised Classification with Graph Convolutional Networks
62
A renaissance of neural networks in drug discovery
63
Molecular graph convolutions: moving beyond fingerprints
64
Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation
65
Order Matters: Sequence to sequence for sets
66
N3 and BNN: Two New Similarity Based Classification Methods in Comparison with Other Classifiers
67
Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
68
Structure-Based Predictions of Activity Cliffs
69
Prediction of Compound Potency Changes in Matched Molecular Pairs Using Support Vector Regression
70
Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde?
71
Deep learning in neural networks: An overview
72
Recent progress in understanding activity cliffs and their utility in medicinal chemistry.
73
Estimating Error Rates in Bioactivity Databases
74
Do medicinal chemists learn from activity cliffs? A systematic evaluation of cliff progression in evolving compound data sets.
75
Extending the Activity Cliff Concept: Structural Categorization of Activity Cliffs and Systematic Identification of Different Types of Cliffs in the ChEMBL Database
76
MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs
77
Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest
78
Exploring activity cliffs in medicinal chemistry.
79
Algorithms for Hyper-Parameter Optimization
80
ChEMBL: a large-scale bioactivity database for drug discovery
81
Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models
82
Scikit-learn: Machine Learning in Python
83
Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
84
Extended-Connectivity Fingerprints
85
Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets
86
Navigating structure-activity landscapes.
87
ImageNet: A large-scale hierarchical image database
88
Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection
89
Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis
90
Structure-Activity Landscape Index: Identifying and Quantifying Activity Cliffs
91
A Normalized Levenshtein Distance Metric
92
On Outliers and Activity Cliffs-Why QSAR Often Disappoints
93
Local Lazy Regression: Making Use of the Neighborhood to Improve QSAR Predictions
94
Reoptimization of MDL Keys for Use in Drug Discovery
95
Prediction of 'drug-likeness'.
96
Greedy function approximation: A gradient boosting machine.
97
An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods
98
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
99
Prediction of Physicochemical Parameters by Atomic Contributions
100
Unsupervised Data Base Clustering Based on Daylight's Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets
101
Long Short-Term Memory
103
The properties of known drugs. 1. Molecular frameworks.
104
Statistical treatment for rejection of deviant values: critical values of Dixon's "Q" parameter and related subrange ratios at the 95% confidence level
105
Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties
106
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
107
FS-Mol: A Few-Shot Learning Dataset of Molecules
109
Molecular fingerprint similarity search in virtual screening.
110
Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing / Volume II: Appendices, References
111
New 3D Molecular Descriptors: The WHIM theory and QSAR Applications
112
Concepts and applications of molecular similarity
113
ITERATIVE PARTIAL EQUALIZATION OF ORBITAL ELECTRONEGATIVITY – A RAPID ACCESS TO ATOMIC CHARGES
114
RDKit: Open-source cheminformatics
