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Pandiyan S, Wang L. In-silico design of novel potential HDAC inhibitors from indazole derivatives targeting breast cancer through QSAR, molecular docking and pharmacokinetics studies. Comput Biol Chem 2024; 110:108035. [PMID: 38460437 DOI: 10.1016/j.compbiolchem.2024.108035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 03/11/2024]
Abstract
Latest studies confirmed that abnormal function of histone deacetylase (HDAC) plays a pivotal role in formation of tumors and is a potential therapeutic target for treating breast cancer. In this research, in-silico drug discovery approaches via quantitative structure activity relationship (QSAR) and molecular docking simulations were adapted to 43 compounds of indazole derivatives with HDAC inhibition for anticancer activity against breast cancer. The QSAR models were built from multiple linear regression (MLR), and models predictability was cross-validated by leave-one-out (LOO) method. Based on these results, compounds C32, C26 and C31 from model 3 showed superior inhibitory activity with pIC50 of 9.30103, 9.1549 and 9.1549. We designed 10 novel compounds with molecular docking scores ranging from -7.9 to -9.3 kcal/mol. The molecular docking simulation results reveal that amino acid residues ILE1122 and PRO1123 play a significant role in bonding with 6CE6 protein. Furthermore, newly designed compounds P5, P2 and P7 with high docking scores of -9.3 kcal/mol, -8.9 kcal/mol and -8.8 kcal/mol than FDA-approved drug Raloxifene (-8.5 kcal/mol) and aid in establishment of potential drug candidate for HDAC inhibitors. The in-silico ADME functionality is used in the final phase to evaluate newly designed inhibitors as potential drug candidates. The results suggest that newly designed compounds P5, P2 and P7 can be used as a potential anti-breast cancer drug candidate.
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Affiliation(s)
- Sanjeevi Pandiyan
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China.
| | - Li Wang
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China
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2
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Rovenchak A, Druchok M. Machine learning-assisted search for novel coagulants: When machine learning can be efficient even if data availability is low. J Comput Chem 2024; 45:937-952. [PMID: 38174834 DOI: 10.1002/jcc.27292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 01/05/2024]
Abstract
Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect-perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data-driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target. For example, there are many known Thrombin inhibitors (acting as anticoagulants), but a very limited number of known Protein C inhibitors (coagulants). In this study, we present our approach to suggest new inhibitor candidates by building an effective representation of chemical space. For this aim, we developed a deep learning model-autoencoder, trained on a large set of molecules in the SMILES format to map the chemical space. Further, we applied different sampling strategies to generate novel coagulant candidates. Symmetrically, we tested our approach on anticoagulant candidates, where we were able to predict their inhibition towards Thrombin. We also compare our approach with MegaMolBART-another deep learning generative model, but exploiting similar principles of navigation in a chemical space.
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Affiliation(s)
- Andrij Rovenchak
- SoftServe, Inc., Lviv, Ukraine
- Professor Ivan Vakarchuk Department for Theoretical Physics, Ivan Franko National University of Lviv, Lviv, Ukraine
| | - Maksym Druchok
- SoftServe, Inc., Lviv, Ukraine
- Institute for Condensed Matter Physics, Lviv, Ukraine
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3
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Duo L, Chen Y, Liu Q, Ma Z, Farjudian A, Ho WY, Low SS, Ren J, Hirst JD, Xie H, Tang B. Discovery of novel SOS1 inhibitors using machine learning. RSC Med Chem 2024; 15:1392-1403. [PMID: 38665844 PMCID: PMC11042245 DOI: 10.1039/d4md00063c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/14/2024] [Indexed: 04/28/2024] Open
Abstract
Overactivation of the rat sarcoma virus (RAS) signaling is responsible for 30% of all human malignancies. Son of sevenless 1 (SOS1), a crucial node in the RAS signaling pathway, could modulate RAS activation, offering a promising therapeutic strategy for RAS-driven cancers. Applying machine learning (ML)-based virtual screening (VS) on small-molecule databases, we selected a random forest (RF) regressor for its robustness and performance. Screening was performed with the L-series and EGFR-related datasets, and was extended to the Chinese National Compound Library (CNCL) with more than 1.4 million compounds. In addition to a series of documented SOS1-related molecules, we uncovered nine compounds that have an unexplored chemical framework and displayed inhibitory activity, with the most potent achieving more than 50% inhibition rate in the KRAS G12C/SOS1 PPI assay and an IC50 value in the proximity of 20 μg mL-1. Compared with the manner that known inhibitory agents bind to the target, hit compounds represented by CL01545365 occupy a unique pocket in molecular docking. An in silico drug-likeness assessment suggested that the compound has moderately favorable drug-like properties and pharmacokinetic characteristics. Altogether, our findings strongly support that, characterized by the distinctive binding modes, the recognition of novel skeletons from the carboxylic acid series could be candidates for developing promising SOS1 inhibitors.
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Affiliation(s)
- Lihui Duo
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
| | - Yi Chen
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 555 Zuchongzhi Road 201203 Shanghai China
- University of Chinese Academy of Sciences No.19A Yuquan Road Beijing 100049 China
| | - Qiupei Liu
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 555 Zuchongzhi Road 201203 Shanghai China
| | - Zhangyi Ma
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
| | - Amin Farjudian
- School of Mathematics, Watson Building, University of Birmingham Edgbaston Birmingham B15 2TT UK
| | - Wan Yong Ho
- Faculty of Medicine and Health Sciences, University of Nottingham (Malaysia Campus) Semenyih 43500 Malaysia
| | - Sze Shin Low
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
| | - Jianfeng Ren
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
| | - Jonathan D Hirst
- School of Chemistry, University of Nottingham University Park Nottingham NG7 2RD UK
| | - Hua Xie
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 555 Zuchongzhi Road 201203 Shanghai China
- University of Chinese Academy of Sciences No.19A Yuquan Road Beijing 100049 China
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences Zhongshan Tsuihang New District Zhongshan 528400 China
| | - Bencan Tang
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
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Jahan I, Laskar MTR, Peng C, Huang JX. A comprehensive evaluation of large Language models on benchmark biomedical text processing tasks. Comput Biol Med 2024; 171:108189. [PMID: 38447502 DOI: 10.1016/j.compbiomed.2024.108189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/14/2024] [Accepted: 02/18/2024] [Indexed: 03/08/2024]
Abstract
Recently, Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks. However, despite their success across various tasks, no prior work has investigated their capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of LLMs on benchmark biomedical tasks. For this purpose, a comprehensive evaluation of 4 popular LLMs in 6 diverse biomedical tasks across 26 datasets has been conducted. To the best of our knowledge, this is the first work that conducts an extensive evaluation and comparison of various LLMs in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot LLMs even outperform the current state-of-the-art models when they were fine-tuned only on the training set of these datasets. This suggests that pre-training on large text corpora makes LLMs quite specialized even in the biomedical domain. We also find that not a single LLM can outperform other LLMs in all tasks, with the performance of different LLMs may vary depending on the task. While their performance is still quite poor in comparison to the biomedical models that were fine-tuned on large training sets, our findings demonstrate that LLMs have the potential to be a valuable tool for various biomedical tasks that lack large annotated data.
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Affiliation(s)
- Israt Jahan
- Department of Biology, York University, Canada; Information Retrieval and Knowledge Management Research Lab, York University, Canada.
| | - Md Tahmid Rahman Laskar
- School of Information Technology, York University, Canada; Information Retrieval and Knowledge Management Research Lab, York University, Canada; Dialpad Inc., Canada.
| | - Chun Peng
- Department of Biology, York University, Canada.
| | - Jimmy Xiangji Huang
- School of Information Technology, York University, Canada; Information Retrieval and Knowledge Management Research Lab, York University, Canada.
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Oloulade BM, Gao J, Chen J, Al-Sabri R, Wu Z. Cancer drug response prediction with surrogate modeling-based graph neural architecture search. Bioinformatics 2023; 39:btad478. [PMID: 37555809 PMCID: PMC10432359 DOI: 10.1093/bioinformatics/btad478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/01/2023] [Accepted: 08/08/2023] [Indexed: 08/10/2023] Open
Abstract
MOTIVATION Understanding drug-response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building an optimal handcrafted GNN model for a particular drug sensitivity dataset requires manual design and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and requires expert knowledge. RESULTS In this work, we propose AutoCDRP, a novel framework for automated cancer drug-response predictor using GNNs. Our approach leverages surrogate modeling to efficiently search for the most effective GNN architecture. AutoCDRP uses a surrogate model to predict the performance of GNN architectures sampled from a search space, allowing it to select the optimal architecture based on evaluation performance. Hence, AutoCDRP can efficiently identify the optimal GNN architecture by exploring the performance of all GNN architectures in the search space. Through comprehensive experiments on two benchmark datasets, we demonstrate that the GNN architecture generated by AutoCDRP surpasses state-of-the-art designs. Notably, the optimal GNN architecture identified by AutoCDRP consistently outperforms the best baseline architecture from the first epoch, providing further evidence of its effectiveness. AVAILABILITY AND IMPLEMENTATION https://github.com/BeObm/AutoCDRP.
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Affiliation(s)
| | - Jianliang Gao
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jiamin Chen
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Raeed Al-Sabri
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Zhenpeng Wu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Wang L, Song Y, Wang H, Zhang X, Wang M, He J, Li S, Zhang L, Li K, Cao L. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals (Basel) 2023; 16:253. [PMID: 37259400 PMCID: PMC9963982 DOI: 10.3390/ph16020253] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 10/13/2023] Open
Abstract
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Kang Li
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
| | - Lei Cao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
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Dixit R, Khambhati K, Supraja KV, Singh V, Lederer F, Show PL, Awasthi MK, Sharma A, Jain R. Application of machine learning on understanding biomolecule interactions in cellular machinery. BIORESOURCE TECHNOLOGY 2023; 370:128522. [PMID: 36565819 DOI: 10.1016/j.biortech.2022.128522] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.
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Affiliation(s)
- Rewati Dixit
- Waste Treatment Laboratory, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Haus-khas, New Delhi 110016, India
| | - Khushal Khambhati
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana 382715, Gujarat, India
| | - Kolli Venkata Supraja
- Waste Treatment Laboratory, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Haus-khas, New Delhi 110016, India
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana 382715, Gujarat, India
| | - Franziska Lederer
- Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Bautzner landstrasse 400, 01328 Dresden, Germany
| | - Pau-Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India; Department of Chemical and Environmental Engineering, University of Nottingham, Malaysia, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
| | - Abhinav Sharma
- Institute Theory of Polymers, Leibniz Institute for Polymer Research, Hohe Strasse 6, 01069 Dresden, Germany
| | - Rohan Jain
- Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Bautzner landstrasse 400, 01328 Dresden, Germany.
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Ranjbarzadeh R, Dorosti S, Jafarzadeh Ghoushchi S, Caputo A, Tirkolaee EB, Ali SS, Arshadi Z, Bendechache M. Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods. Comput Biol Med 2023; 152:106443. [PMID: 36563539 DOI: 10.1016/j.compbiomed.2022.106443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/24/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
The Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case.
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Affiliation(s)
- Ramin Ranjbarzadeh
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | - Shadi Dorosti
- Department of Industrial Engineering, Urmia University of Technology, Urmia, Iran.
| | | | - Annalina Caputo
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | | | - Sadia Samar Ali
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Zahra Arshadi
- Faculty of Electronics, Telecommunications and Physics Engineering, Polytechnic University, Turin, Italy.
| | - Malika Bendechache
- Lero & ADAPT Research Centres, School of Computer Science, University of Galway, Ireland.
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