![]() ![]() In Advances in Neural Information Processing Systems Vol. Graph convolutional policy network for goal-directed molecular graph generation. In International Conference on Machine Learning Vol. Junction tree variational autoencoder for molecular graph generation. Optimization of molecules via deep reinforcement learning. MolGAN: An implicit generative model for small molecular graphs. In International Conference on Artificial Neural Networks Vol. GraphVAE: towards generation of small graphs using variational autoencoders. Learning deep generative models of graphs. Li, Y., Vinyals, O., Dyer, C., Pascanu, R. Convolutional embedding of attributed molecular graphs for physical property prediction. 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 3585–3594 (Association for Computing Machinery, 2021) Ĭoley, C. MoCL: data-driven molecular fingerprint via knowledge-aware contrastive learning from molecular graph. Xai for graphs: explaining graph neural network predictions by identifying relevant walks. Coloring molecules with explainable artificial intelligence for preclinical relevance assessment. Jiménez-Luna, J., Skalic, M., Weskamp, N. Drug discovery with explainable artificial intelligence. Molecular machine learning with conformer ensembles. Analyzing learned molecular representations for property prediction. Quantum chemical accuracy from density functional approximations via machine learning. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. Schütt, K., Gastegger, M., Tkatchenko, A., Müller, K.-R. SchNet–a deep learning architecture for molecules and materials. Equivariant message passing for the prediction of tensorial properties and molecular spectra. PhysNet: a neural network for predicting energies, forces, dipole moments, and partial charges. Chemi-Net: a molecular graph convolutional network for accurate drug property prediction. Learning graph-level representation for drug discovery. A graph-convolutional neural network model for the prediction of chemical reactivity. Learning graph models for retrosynthesis prediction. A deep learning approach to antibiotic discovery. Graph convolutional neural networks for predicting drug-target interactions. PotentialNet for molecular property prediction. In International Conference on Learning Representations Vol. Directional message passing for molecular graphs. Quantum-chemical insights from deep tensor neural networks. T., Arbabzadah, F., Chmiela, S., Müller, K. Graph neural networks: a review of methods and applications. Relational inductive biases, deep learning, and graph networks. Interaction networks for learning about objects, relations and physics. IEEE Conference on Computer Vision and Pattern Recognition 5115–5124 (2017).īattaglia, P. Geometric deep learning on graphs and manifolds using mixture model CNNs. In International Conference on Neural Information Processing Systems, Vol. Convolutional networks on graphs for learning molecular fingerprints. In International Conference on Learning Representations Vol 5. Semi-supervised classification with graph convolutional networks. Approximation by superpositions of a sigmoidal function. Simple method of calculating octanol/water partition coefficient. Moriguchi, I., Hirono, S., Liu, Q., Nakagome, I. On the generalization of equivariance and convolution in neural networks to the action of compact groups. Group equivariant convolutional networks. ![]() 34 (Springer Science & Business Media, 1994).Ĭohen, T. Geometric deep learning: grids, groups, graphs, geodesics, and gauges. Generating focused molecule libraries for drug discovery with recurrent neural networks. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Geometric deep learning of RNA structure. Molecular Descriptors for Chemoinformatics Vols I–II (John Wiley & Sons, 2009). Geometric deep learning: going beyond euclidean data. Imagenet classification with deep convolutional neural networks. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. Highly accurate protein structure prediction with AlphaFold. Neural message passing for quantum chemistry. ![]() Deep learning in neural networks: an overview. ![]()
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