Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
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BACKGROUND: In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. The aim of this work was dual, first large number of hyper-parameter configurations were explored to investigate how they affect the performance of DNNs and could act as starting points when tuning DNNs and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naïve Bayes, k-nearest neighbor, random forest and support vector machines. Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed. The open-source Caffe deep-learning framework and modern NVidia GPU units were utilized to carry out this study, allowing large number of DNN configurations to be explored. RESULTS: We show that feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized. Hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons. When compared to the rest methods, tuned DNNs were found to statistically outperform, with p value <0.01 based on Wilcoxon statistical test. DNN achieved on average MCC units of 0.149 higher than NB, 0.092 than kNN, 0.052 than SVM with linear kernel, 0.021 than RF and finally 0.009 higher than SVM with radial basis function kernel. When exploring robustness to noise, non-linear methods were found to perform well when dealing with low levels of noise, lower than or equal to 20%, however when dealing with higher levels of noise, higher than 30%, the Naïve Bayes method was found to perform well and even outperform at the highest level of noise 50% more sophisticated methods across several datasets.
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In our assessment of different machine learning methods and molecular descriptors with 18,886 compounds screened against Mycobacterium tuberculosis, DNNs and support vector machines appeared to be superior methods regardless of the descriptor type for training and 5-fold cross validation.
“This comparison demonstrated that DNNs and support vector machines appear to be superior methods regardless of the descriptor type for training and 5-fold cross validation.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [51]
External testing of DNN models with a large test set did not perform as well as other machine learning methods.
“Conversely, external testing of DNN models with a large test set did not perform as well as other machine learning methods.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [51]
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- Authors
- Koutsoukas, A., Monaghan, K. J., Li, X., & Huan, J.
- Affiliations
- University of Kansas (US)
- Corresponding author
- Huan, Jun
- ORCID
- Venue
- Journal of Cheminformatics
- Published
- 2017-06-28
- Volume
- 9
- Issue
- 1
- Pages
- 42-42
- ISSN
- 1758-2946 (ISSN-L)
- PMC
- PMC5489441
- OpenAlex
- W2731161895
- Cited by
- 315
- References
- 69
- Citations by year
- 2026: 12 · 2025: 25 · 2024: 40 · 2023: 42 · 2022: 53 · 2021: 55 · 2020: 32 · 2019: 33 · 2018: 22 · 2017: 1
- FWCI
- 18.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Cheminformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (CNS 1337899)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- deep neural network, hyperparameter optimization, bioactivity prediction, QSAR, predictive toxicology, feedforward neural networks, noise robustness, activation functions, dropout regularization, Caffe, drug discovery, naive Bayes, k-nearest neighbors, random forest, support vector machine
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