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Kehrein J, Bunker A, Luxenhofer R. POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles. Mol Pharm 2024; 21:3356-3374. [PMID: 38805643 DOI: 10.1021/acs.molpharmaceut.4c00086] [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] [Indexed: 05/30/2024]
Abstract
Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.
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Affiliation(s)
- Josef Kehrein
- Soft Matter Chemistry, Department of Chemistry, Faculty of Science, University of Helsinki, A. I. Virtasen aukio 1, 00014 Helsinki, Finland
- Drug Research Program, Division of Pharmaceutical Biosciences Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, 00014 Helsinki, Finland
| | - Alex Bunker
- Drug Research Program, Division of Pharmaceutical Biosciences Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, 00014 Helsinki, Finland
| | - Robert Luxenhofer
- Soft Matter Chemistry, Department of Chemistry, Faculty of Science, University of Helsinki, A. I. Virtasen aukio 1, 00014 Helsinki, Finland
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2
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Daghighi A, Casanola-Martin GM, Iduoku K, Kusic H, González-Díaz H, Rasulev B. Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:10116-10127. [PMID: 38797941 DOI: 10.1021/acs.est.4c01017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.
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Affiliation(s)
- Amirreza Daghighi
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Hrvoje Kusic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, Zagreb 10000, Croatia
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa 48940, Spain
- BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, Leioa 48940, Spain
- IKERBASQUE, Basque Foundation for Science,Bilbao, Biscay 48011, Spain
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
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3
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Ghosh S, Roy K. Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats. Toxicology 2024; 505:153824. [PMID: 38705560 DOI: 10.1016/j.tox.2024.153824] [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: 03/27/2024] [Revised: 04/28/2024] [Accepted: 04/29/2024] [Indexed: 05/07/2024]
Abstract
We have developed a quantitative safety prediction model for subchronic repeated doses of diverse organic chemicals on rats using the novel quantitative read-across structure-activity relationship (q-RASAR) approach, which uses similarity-based descriptors for predictive model generation. The experimental -Log (NOAEL) values have been used here as a potential indicator of oral subchronic safety on rats as it determines the maximum dose level for which no observed adverse effects of chemicals are found. A total of 186 data points of diverse organic chemicals have been used for the model generation using structural and physicochemical (0D-2D) descriptors. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D-2D descriptors. Then, the combined pool of RASAR and the identified 0D-2D descriptors of the training set were employed to develop the final models by using the partial least squares (PLS) algorithm. The developed PLS model was rigorously validated by various internal and external validation metrics as suggested by the Organization for Economic Co-operation and Development (OECD). The final q-RASAR model is proven to be statistically sound, robust and externally predictive (R2 = 0.85, Q2LOO = 0.82 and Q2F1 = 0.94), superseding the internal as well as external predictivity of the corresponding quantitative structure-activity relationship (QSAR) model as well as previously reported subchronic repeated dose toxicity model found in the literature. In a nutshell, the q-RASAR is an effective approach that has the potential to be used as a good alternative way to improve external predictivity, interpretability, and transferability for subchronic oral safety prediction as well as ecotoxicity risk identification.
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Affiliation(s)
- Shilpayan Ghosh
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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Talebibarmi P, Vahidi B, Ebad M. In silico analysis of embolism in cerebral arteries using fluid-structure interaction method. Heliyon 2024; 10:e30443. [PMID: 38720729 PMCID: PMC11077041 DOI: 10.1016/j.heliyon.2024.e30443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
Ischemic stroke, particularly embolic stroke, stands as a significant global contributor to mortality and long-term disabilities. This paper presents a comprehensive simulation of emboli motion through the middle cerebral artery (MCA), a prevalent site for embolic stroke. Our patient-specific computational model integrates major branches of the middle cerebral artery reconstructed from magnetic resonance angiography images, pulsatile flow dynamics, and emboli of varying geometries, sizes, and material properties. The fluid-structure interactions method is employed to simulate deformable emboli motion through the middle cerebral artery, allowing observation of hemodynamic changes in artery branches upon embolus entry. We investigated the impact of embolus presence on shear stress magnitude on artery walls, analyzed the effects of embolus material properties and geometries on embolus trajectory and motion dynamics within the middle cerebral artery. Additionally, we evaluated the non-Newtonian behavior of blood, comparing it with Newtonian blood behavior. Our findings highlight that embolus geometry significantly influences trajectory, motion patterns, and hemodynamics within middle cerebral artery branches. Emboli with visco-hyperelastic material properties experienced higher stresses upon collision with artery walls compared to those with hyperelastic properties. Furthermore, considering blood as a non-Newtonian fluid had notable effects on emboli stresses and trajectories within the artery, particularly during collisions. Notably, the maximum von Mises stress experienced in our study was 21.83 kPa, suggesting a very low probability of emboli breaking during movement, impact, and after coming to a stop. However, in certain situations, the magnitude of shear stress on them exceeded 1 kPa, increasing the likelihood of cracking and disintegration. These results serve as an initial step in anticipating critical clinical conditions arising from arterial embolism in the middle cerebral artery. They provide insights into the biomechanical parameters influencing embolism, contributing to improved clinical decision-making for stroke management.
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Affiliation(s)
- Pouria Talebibarmi
- Division of Biomedical Engineering, Department of Life Science Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | - Bahman Vahidi
- Division of Biomedical Engineering, Department of Life Science Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | - Mahtab Ebad
- Division of Biomedical Engineering, Department of Life Science Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
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Erickson M, Casañola-Martin G, Han Y, Rasulev B, Kilin D. Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems. J Phys Chem B 2024; 128:2190-2200. [PMID: 38386478 DOI: 10.1021/acs.jpcb.3c06854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we develop a procedure to better understand the photodegradation reactions combining density functional theory (DFT) based time-dependent excited-state molecular dynamics (TDESMD) studies with machine learning-based quantitative structure-activity relationships (QSAR) methodology. This procedure allows for the unveiling of hidden structural features between active orbitals that affect the rate of photodegradation and is coined InfoTDESMD. Findings show that electrotopological features are influential factors affecting the rate of photodegradation in differing environments. Additionally, statistical validations and knowledge-based analysis of descriptors are conducted to further understand the structural features' influence on the rate of photodegradation of polymeric materials.
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Affiliation(s)
- Meade Erickson
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Gerardo Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Yulun Han
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Dmitri Kilin
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States
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Rodríguez-Belenguer P, March-Vila E, Pastor M, Mangas-Sanjuan V, Soria-Olivas E. Usage of model combination in computational toxicology. Toxicol Lett 2023; 389:34-44. [PMID: 37890682 DOI: 10.1016/j.toxlet.2023.10.013] [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: 09/08/2023] [Revised: 10/17/2023] [Accepted: 10/24/2023] [Indexed: 10/29/2023]
Abstract
New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.
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Affiliation(s)
- Pablo Rodríguez-Belenguer
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain; Department of Pharmacy and Pharmaceutical Technology and Parasitology, Universitat de València, 46100 Valencia, Spain
| | - Eric March-Vila
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain
| | - Victor Mangas-Sanjuan
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, Universitat de València, 46100 Valencia, Spain; Interuniversity Research Institute for Molecular Recognition and Technological Development, Universitat Politècnica de València, 46100 Valencia, Spain
| | - Emilio Soria-Olivas
- IDAL, Intelligent Data Analysis Laboratory, ETSE, Universitat de València, 46100 Valencia, Spain.
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Diem-Tran PT, Ho TT, Tuan NV, Bao LQ, Phuong HT, Chau TTG, Minh HTB, Nguyen CT, Smanova Z, Casanola-Martin GM, Rasulev B, Pham-The H, Cuong LCV. Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands. TOXICS 2023; 11:595. [PMID: 37505560 PMCID: PMC10383909 DOI: 10.3390/toxics11070595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/02/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023]
Abstract
Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβML) and potentiometric sensitivity (PSML) of 200 ligands in complexes with the heavy metal ions Cu2+, Cd2+, and Pb2+. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R2) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R2 of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.
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Affiliation(s)
- Phan Thi Diem-Tran
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Tue-Tam Ho
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Nguyen-Van Tuan
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Le-Quang Bao
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Ha Tran Phuong
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Trinh Thi Giao Chau
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Hoang Thi Binh Minh
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Cong-Truong Nguyen
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Zulayho Smanova
- Department of Chemistry, National University of Uzbekistan after Mirzo Ulugbek, Tashkent 100012, Uzbekistan
| | | | - Bakhtiyor Rasulev
- Department of Chemistry, National University of Uzbekistan after Mirzo Ulugbek, Tashkent 100012, Uzbekistan
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Hai Pham-The
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Le Canh Viet Cuong
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
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Kumar A, Kumar V, Podder T, Ojha PK. First report on ecotoxicological QSTR and I-QSTR modeling for the prediction of acute ecotoxicity of diverse organic chemicals against three protozoan species. CHEMOSPHERE 2023:139066. [PMID: 37257655 DOI: 10.1016/j.chemosphere.2023.139066] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/15/2023] [Accepted: 05/27/2023] [Indexed: 06/02/2023]
Abstract
The recent years have witnessed an upsurge of interest to assess the toxicity of organic chemicals exhibiting harmful impacts on the environment. In this investigation, we have developed regression-based quantitative structure-toxicity relationship (QSTR) models against three protozoan species (Entosiphon sulcantum, Uronema parduczi, and Chilomonas paramecium) using three sets of descriptor combinations such as ETA indices only, non-ETA descriptors only, and both ETA and non-ETA descriptors to examine the key structural features that determine the toxic properties of protozoa. The interspecies models (i-QSTRs) were also generated for efficient data gap-filling of toxicity databases. The statistical results of the validated models in terms of both internal and external validation metrics suggested that the models are statistically reliable and robust. Additionally, using these validated models, we screened the DrugBank database containing 11,300 pharmaceuticals for assessing the ecotoxicological properties. The features appearing in the models suggested that nonpolar characteristics, electronegativity, hydrogen bonding, π-π, and hydrophobic interactions are responsible for chemical toxicity toward protozoan. The validated models may be utilized for the development of eco-friendly drugs & chemicals, data gap-filling of toxicity databases for regulatory purposes and research, as well as to decrease the use of toxic and hazardous chemicals in the environment.
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Affiliation(s)
- Ankur Kumar
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics Laboratory (DTC Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Trina Podder
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Probir Kumar Ojha
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships. Molecules 2023; 28:molecules28052410. [PMID: 36903654 PMCID: PMC10005768 DOI: 10.3390/molecules28052410] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/09/2023] Open
Abstract
A deep learning-based quantitative structure-activity relationship analysis, namely the molecular image-based DeepSNAP-deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a three-dimensional (3D) structure of a chemical compound. It allows building high-performance prediction models without extracting and selecting features because of its powerful feature discrimination capability. Deep learning (DL) is based on a neural network with multiple intermediate layers that makes it possible to solve highly complex problems and improve the prediction accuracy by increasing the number of hidden layers. However, DL models are too complex when it comes to understanding the derivation of predictions. Instead, molecular descriptor-based machine learning has clear features owing to the selection and analysis of features. However, molecular descriptor-based machine learning has some limitations in terms of prediction performance, calculation cost, feature selection, etc., while the DeepSNAP-deep learning method outperforms molecular descriptor-based machine learning due to the utilization of 3D structure information and the advanced computer processing power of DL.
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