Virtual Screening of DPP-4 Inhibitors Using QSAR-Based Artificial Intelligence and Molecular Docking of Hit Compounds to DPP-8 and DPP-9 Enzymes

Hermansyah, Oky; Bustamam, Alhadi; Yanuar, Arry

doi:10.21203/rs.2.22282/v1

Cited by 5 publications

(1 citation statement)

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“…The analysis of the QSAR method is generally categorised into QSAR regression and QSAR classification [29]. The QSAR classification method is used to predict the active compound from the database, and then the QSAR regression method is used to study the activity value [30]. This study aims to build QSAR models using an artificial intelligence paradigm, especially for the QSAR classification and regression models, to design a new DPP-4 inhibitor candidate for the treatment of type 2 diabetes.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Paradigm for Ligand-Based Virtual Screening on the Drug Discovery of Type 2 Diabetes Mellitus

Bustamam

Hamzah

Husna

et al. 2021

Preprint

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Background: New dipeptidyl peptidase-4 (DPP-4) inhibitors need to be developed to be used as agents with low adverse effects for the treatment of type 2 diabetes mellitus. This study aims to build quantitative structure-activity relationship (QSAR) models using the artificial intelligence paradigm. Random Forest and Deep Neural Network are used to predict QSAR models. We compared principal component analysis (PCA) with sparse PCA as methods for transforming Rotation Forest. K-modes clustering with Levenshtein distance was used for the selection method of molecules, and CatBoost was used for the feature selection method. Results: The amount of the DPP-4 inhibitor molecules resulting from the selection process of molecules using K-Modes clustering algorithm is 1020 with logP range value of -1.6693 to 4.99044. Several fingerprint methods such as extended connectivity fingerprint and functional class fingerprint with diameters of 4 and 6 were used to construct four fingerprint datasets, ECFP_4, ECFP_6, FCFP_4, and FCFP_6. There are 1024 features from the four fingerprint datasets that are then selected using the CatBoost method. CatBoost can represent QSAR models with good performance for machine learning and deep learning methods respectively with evaluation metrics, such as Sensitivity, Specificity, Accuracy, and Matthew's correlation coefficient, all valued above 70% with a feature importance level of 60%, 70%, 80%, and 90%.Conclusions: The K-Modes clustering algorithm can produce a representative subset of DPP-4 inhibitor molecules. Feature selection in the fingerprint dataset using CatBoost is best used before making QSAR Classification and QSAR Regression models. QSAR Classification using Machine Learning and QSAR Classification using Deep Learning, each of which has an accuracy of above 70%. The Rotation Forest (PCA) model performed better than the Rotation Forest (Sparse PCA) model, both in the QSAR Classification and QSAR Regression model because the Rotation Forest (PCA) has a more effective time than the Rotation Forest (Sparse PCA).

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Paradigm for Ligand-Based Virtual Screening on the Drug Discovery of Type 2 Diabetes Mellitus

Bustamam

Hamzah

Husna

et al. 2021

Preprint

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Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

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Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

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Extracting Predictive Representations from Hundreds of Millions of Molecules

Chen

Zheng

Wei

et al. 2021

J. Phys. Chem. Lett.

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The construction of appropriate representations remains essential for molecular predictions due to intricate molecular complexity. Additionally, it is often expensive and ethically constrained to generate labeled data for supervised learning in molecular sciences, leading to challenging small and diverse data sets. In this work, we develop a self-supervised learning approach to pretrain models from over 700 million unlabeled molecules in multiple databases. The intrinsic chemical logic learned from this approach enables the extraction of predictive representations from task-specific molecular sequences in a fine-tuned process. To understand the importance of self-supervised learning from unlabeled molecules, we assemble three models with different combinations of databases. Moreover, we propose a protocol based on data traits to automatically select the optimal model for a specific task. To validate the proposed method, we consider 10 benchmarks and 38 virtual screening data sets. Extensive validation indicates that the proposed method shows superb performance.

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Virtual Screening of DPP-4 Inhibitors Using QSAR-Based Artificial Intelligence and Molecular Docking of Hit Compounds to DPP-8 and DPP-9 Enzymes

Cited by 5 publications

References 0 publications

Artificial Intelligence Paradigm for Ligand-Based Virtual Screening on the Drug Discovery of Type 2 Diabetes Mellitus

Artificial Intelligence Paradigm for Ligand-Based Virtual Screening on the Drug Discovery of Type 2 Diabetes Mellitus

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Extracting Predictive Representations from Hundreds of Millions of Molecules

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