# Deep Learning for Computational Chemistry

arXiv: https://arxiv.org/abs/1701.04503 · HTML: https://mlsanity.com/paper/1701.04503/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2017.

## Built on by (most cited first, 30 of 38)

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- [Physics Informed Neural Network for Dynamic Stress Prediction](https://mlsanity.com/paper/2211.16190/) (2022)
- [Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation](https://mlsanity.com/paper/2311.02798/) (2023)
- [Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures](https://mlsanity.com/paper/2304.12576/) (2023)
- [MolVision: Molecular Property Prediction with Vision Language Models](https://mlsanity.com/paper/2507.03283/) (2025)
- [Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?](https://mlsanity.com/paper/2306.17702/) (2023)
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- [Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate](https://mlsanity.com/paper/2202.10244/) (2022)
- [Variance Tolerance Factors For Interpreting ALL Neural Networks](https://mlsanity.com/paper/2209.13858/) (2022)
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- [A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition](https://mlsanity.com/paper/2405.07260/) (2024)
- [Acoustic Structure Inverse Design and Optimization Using Deep Learning](https://mlsanity.com/paper/2102.02063/) (2021)
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- [Waveflow: boundary-conditioned normalizing flows applied to fermionic wavefunctions](https://mlsanity.com/paper/2211.14839/) (2022)
- [Gemtelligence: Accelerating Gemstone classification with Deep Learning](https://mlsanity.com/paper/2306.06069/) (2023)
- [MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures](https://mlsanity.com/paper/2311.16666/) (2023)
- [Exploring the Relationship: Transformative Adaptive Activation Functions in Comparison to Other Activation Functions](https://mlsanity.com/paper/2402.09249/) (2024)
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- [Automatic design of novel potential 3CLpro and PLpro inhibitors](https://mlsanity.com/paper/2101.11890/) (2021)
- [Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs](https://mlsanity.com/paper/2110.08244/) (2021)
- [Altering Backward Pass Gradients improves Convergence](https://mlsanity.com/paper/2111.12495/) (2021)
- [Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis](https://mlsanity.com/paper/2212.14623/) (2022)
- [Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components](https://mlsanity.com/paper/2301.02580/) (2022)

## Abstract (arXiv)

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure prediction, quantum chemistry, materials design and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry.