An explainable molecular property prediction via multi-granularity

[1]  Eyal Fisher,et al.  What is Interpretability? , 2020, Philosophy & Technology.

[2]  Ola Engkvist,et al.  Molecular representations in AI-driven drug discovery: a review and practical guide , 2020, Journal of Cheminformatics.

[3]  Jürgen Bajorath,et al.  Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values. , 2020, Journal of medicinal chemistry.

[4]  Igor V. Tetko,et al.  Transformer-CNN: Swiss knife for QSAR modeling and interpretation , 2020, Journal of Cheminformatics.

[5]  Seddik Belkoura,et al.  Fostering interpretability of data mining models through data perturbation , 2019, Expert Syst. Appl..

[6]  Gisbert Schneider,et al.  Mind and machine in drug design , 2019, Nature Machine Intelligence.

[7]  Thomas Blaschke,et al.  The rise of deep learning in drug discovery. , 2018, Drug discovery today.

[8]  Wojciech Samek,et al.  Methods for interpreting and understanding deep neural networks , 2017, Digit. Signal Process..

[9]  Qinghua Hu,et al.  Granular Computing Based Machine Learning in the Era of Big Data , 2017, Inf. Sci..

[10]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[11]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[12]  Robert P. Sheridan,et al.  Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships , 2015, J. Chem. Inf. Model..

[13]  David M. Reif,et al.  Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway , 2014, Scientific Reports.

[14]  Udo Seiffert,et al.  Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size , 2014, Neurocomputing.

[15]  Alex Alves Freitas,et al.  Comprehensible classification models: a position paper , 2014, SKDD.

[16]  Ghazaleh Ghavami,et al.  Artificial neural network modeling of antimycobacterial chemical space to introduce efficient descriptors employed for drug design , 2014 .

[17]  Luis Pinheiro,et al.  A Bayesian Approach to in Silico Blood-Brain Barrier Penetration Modeling , 2012, J. Chem. Inf. Model..

[18]  Lorenz C. Blum,et al.  970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. , 2009, Journal of the American Chemical Society.

[19]  Matthias Rarey,et al.  On the Art of Compiling and Using 'Drug‐Like' Chemical Fragment Spaces , 2008, ChemMedChem.

[20]  Alex M. Clark,et al.  2D Structure Depiction , 2006, J. Chem. Inf. Model..

[21]  John S. Delaney,et al.  ESOL: Estimating Aqueous Solubility Directly from Molecular Structure , 2004, J. Chem. Inf. Model..

[22]  Lotfi A. Zadeh,et al.  Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems , 1998, Soft Comput..

[23]  Michael M. Hann,et al.  RECAP-Retrosynthetic Combinatorial Analysis Procedure: A Powerful New Technique for Identifying Privileged Molecular Fragments with Useful Applications in Combinatorial Chemistry , 1998, J. Chem. Inf. Comput. Sci..

[24]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[25]  David Weininger,et al.  SMILES, 3. DEPICT. Graphical depiction of chemical structures , 1990, J. Chem. Inf. Comput. Sci..

[26]  David Weininger,et al.  SMILES. 2. Algorithm for generation of unique SMILES notation , 1989, J. Chem. Inf. Comput. Sci..

[27]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[28]  S R Heller,et al.  An interactive, versatile, three-dimensional display, manipulation and plotting system for biomedical research. , 1972, Journal of chemical documentation.