Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction
Hentabli, Hamza and Bengherbia, Billel and Saeed, Faisal and Salim, Naomie and Nafea, Ibtehal and Toubal, Abdelmoughni and Nasser, Maged (2022) Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction. International Journal of Molecular Sciences, 23 (21). 13230;. ISSN 1422-0067
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Abstract
Determining and modeling the possible behaviour and actions of molecules requires investigating the basic structural features and physicochemical properties that determine their behaviour during chemical, physical, biological, and environmental processes. Computational approaches such as machine learning methods are alternatives to predicting the physiochemical properties of molecules based on their structures. However, the limited accuracy and high error rates of such predictions restrict their use. In this paper, a novel technique based on a deep learning convolutional neural network (CNN) for the prediction of chemical compounds’ bioactivity is proposed and developed. The molecules are represented in the new matrix format Mol2mat, a molecular matrix representation adapted from the well-known 2D-fingerprint descriptors. To evaluate the performance of the proposed methods, a series of experiments were conducted using two standard datasets, namely the MDL Drug Data Report (MDDR) and Sutherland, datasets comprising 10 homogeneous and 14 heterogeneous activity classes. After analysing the eight fingerprints, all the probable combinations were investigated using the five best descriptors. The results showed that a combination of three fingerprints, ECFP4, EPFP4, and ECFC4, along with a CNN activity prediction process, achieved the highest performance of 98% AUC when compared to the state-of-the-art ML algorithms NaiveB, LSVM, and RBFN
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.3390/ijms232113230 | ||||||
| Dates: |
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| Uncontrolled Keywords: | activity prediction model; biological activities; bioactive molecules; convolutional neural network; deep learning | ||||||
| Subjects: | CAH03 - biological and sport sciences > CAH03-01 - biosciences > CAH03-01-08 - molecular biology, biophysics and biochemistry CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-02 - information technology CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence |
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| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Faisal Saeed | ||||||
| Date Deposited: | 11 Nov 2022 09:58 | ||||||
| Last Modified: | 22 Mar 2023 12:00 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/13732 |
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