1
|
Saeed M, Alamri MA, Rashid MAR, Javed MR, Azeem F, Bashir Z, Alanzi AR, Muhseen ZT, Almusallam SY, Hussain K. Identification of novel inhibitors against VP40 protein of Marburg virus by integrating molecular modeling and dynamics approaches. J Biomol Struct Dyn 2024:1-14. [PMID: 38178383 DOI: 10.1080/07391102.2023.2300134] [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: 05/29/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024]
Abstract
Marburg virus (MV) is a highly etiological agent of haemorrhagic fever in humans and has spread across the world. Its outbreaks caused a 23-90% human death rate. However, there are currently no authorized preventive or curative measures yet. VP40 is the MV matrix protein, which builds protein shell underneath the viral envelope and confers hallmark filamentous. VP40 alone is able to induce assembly and budding of filamentous virus-like particles (VLPs), which resemble authentic virions. As a result, this research is credited with clarifying the function of VP40 and leading to the discovery of new therapeutic targets effective in combating MV disease (MVD). Virtual screening, molecular docking and molecular dynamics (MD) simulation were used to find the putative active chemicals based on a 3D pharmacophore model of the protein's active site cavity. Initially, andrographidine-C, a potent inhibitor was selected for the development of the pharmacophore model. Later, a library of 30,000 compounds along with the andrographidine-C was docked against VP40 protein. Three best hits including avanafil, diuvaretin and macrourone were subjected to further MD simulation analysis, as these compounds had better binding affinities as compared to andrographidine-C. Furthermore, throughout the 100 ns simulations, the back bone of VP40 protein in presence of avanafil, diuvaretin and macrourone remained stable which was further validated by MM-PBSA analysis. Additionally, all of these compounds depict maximum drug-like properties. The predicted drugs based on the ligand, avanafil, diuvaretin and macrourone could be exploited and developed as an alternative or complementary therapy for the treatment of MVD.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Muhammad Saeed
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Mubarak A Alamri
- Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | | | - Muhammad Rizwan Javed
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Farrukh Azeem
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Zarmina Bashir
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Abdullah R Alanzi
- Department of Pharmacogonsy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | | | - Shahad Youseff Almusallam
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia
| | - Khadim Hussain
- Plant Protection Department, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| |
Collapse
|
2
|
Kim MJ, Martin CA, Kim J, Jablonski MM. Computational methods in glaucoma research: Current status and future outlook. Mol Aspects Med 2023; 94:101222. [PMID: 37925783 PMCID: PMC10842846 DOI: 10.1016/j.mam.2023.101222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/06/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.
Collapse
Affiliation(s)
- Minjae J Kim
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Cole A Martin
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Jinhwa Kim
- Graduate School of Artificial Intelligence, Graduate School of Metaverse, Department of Management Information Systems, Sogang University, 1 Shinsoo-Dong, Mapo-Gu, Seoul, South Korea.
| | - Monica M Jablonski
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| |
Collapse
|
3
|
Shah HS, Michelle C, Xie T, Chaturvedi K, Kuang S, Abramov YA. Computational and Experimental Screening Approaches to Aripiprazole Salt Crystallization. Pharm Res 2023; 40:2779-2789. [PMID: 37127778 DOI: 10.1007/s11095-023-03522-z] [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: 02/23/2023] [Accepted: 04/12/2023] [Indexed: 05/03/2023]
Abstract
INTRODUCTION The screening of multicomponent crystal system (MCC) is a key method for improving physicochemical properties of active pharmaceutical ingredients (APIs). The challenges associated with experimental salt screening include a large number of potential counterions and solvent systems and tendency to undergo disproportionation to produce free form during crystallization. These challenges may be mitigated by a combination of experimental and computational approaches to salt screening. The goal of this study is to evaluate performance of the counterion screening methods and propose and validate novel approaches to virtual solvent screening for MCC crystallization. METHODS The actual performance of the ΔpKa > 3 rule for counterion selection was validated using multiple screenings reports. Novel computational models for virtual solvent screening to avoid MCC incongruent crystallization were proposed. Using the ΔpKa rule, 10 acid counterions were selected for experimental aripiprazole (APZ) salt screening using 10 organic solvents. The experimental results were used to validate the proposed novel virtual solvent screen models. RESULTS Experimental APZ salt screening resulted in a total of eight MCCs which included glucuronate, mesylate, oxalate, tartrate, salicylate and mandelate. The new model to virtually screen solvents provided a general agreement with APZ experimental findings in terms of selecting the optimal solvent for MCC crystallization. CONCLUSION The rational selection of counterions and organic solvents for MCC crystallization was presented using combined novel computational model as well as experimental studies. The current virtual solvent screen model was successfully implemented and validated which can be easily applied to newly discovered APIs.
Collapse
Affiliation(s)
- Harsh S Shah
- J-Star Research Inc., 6 Cedarbrook Drive, Cranbury, NJ, 08512, USA.
| | | | - Tian Xie
- J-Star Research Inc., 6 Cedarbrook Drive, Cranbury, NJ, 08512, USA
| | | | - Shanming Kuang
- J-Star Research Inc., 6 Cedarbrook Drive, Cranbury, NJ, 08512, USA
| | - Yuriy A Abramov
- J-Star Research Inc., 6 Cedarbrook Drive, Cranbury, NJ, 08512, USA.
- Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| |
Collapse
|
4
|
Firaha D, Liu YM, van de Streek J, Sasikumar K, Dietrich H, Helfferich J, Aerts L, Braun DE, Broo A, DiPasquale AG, Lee AY, Le Meur S, Nilsson Lill SO, Lunsmann WJ, Mattei A, Muglia P, Putra OD, Raoui M, Reutzel-Edens SM, Rome S, Sheikh AY, Tkatchenko A, Woollam GR, Neumann MA. Predicting crystal form stability under real-world conditions. Nature 2023; 623:324-328. [PMID: 37938708 PMCID: PMC10632141 DOI: 10.1038/s41586-023-06587-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 08/30/2023] [Indexed: 11/09/2023]
Abstract
The physicochemical properties of molecular crystals, such as solubility, stability, compactability, melting behaviour and bioavailability, depend on their crystal form1. In silico crystal form selection has recently come much closer to realization because of the development of accurate and affordable free-energy calculations2-4. Here we redefine the state of the art, primarily by improving the accuracy of free-energy calculations, constructing a reliable experimental benchmark for solid-solid free-energy differences, quantifying statistical errors for the computed free energies and placing both hydrate crystal structures of different stoichiometries and anhydrate crystal structures on the same energy landscape, with defined error bars, as a function of temperature and relative humidity. The calculated free energies have standard errors of 1-2 kJ mol-1 for industrially relevant compounds, and the method to place crystal structures with different hydrate stoichiometries on the same energy landscape can be extended to other multi-component systems, including solvates. These contributions reduce the gap between the needs of the experimentalist and the capabilities of modern computational tools, transforming crystal structure prediction into a more reliable and actionable procedure that can be used in combination with experimental evidence to direct crystal form selection and establish control5.
Collapse
Affiliation(s)
| | | | | | | | | | - Julian Helfferich
- Avant-garde Materials Simulation, Merzhausen, Germany
- JobRad, Freiburg, Germany
| | - Luc Aerts
- UCB Pharma SA, Chemin du Foriest, Braine-l'Alleud, Belgium
| | - Doris E Braun
- Institute of Pharmacy, University of Innsbruck, Innsbruck, Austria
| | - Anders Broo
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, Mölndal, Sweden
| | | | - Alfred Y Lee
- Merck, Analytical Research & Development, Rahway, NJ, USA
| | - Sarah Le Meur
- UCB Pharma SA, Chemin du Foriest, Braine-l'Alleud, Belgium
| | - Sten O Nilsson Lill
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, Mölndal, Sweden
| | | | - Alessandra Mattei
- Solid State Chemistry, Research & Development, AbbVie, North Chicago, IL, USA
| | | | - Okky Dwichandra Putra
- Early Product Development and Manufacturing, Pharmaceutical Sciences R&D, AstraZeneca Gothenburg, Mölndal, Sweden
| | | | - Susan M Reutzel-Edens
- Cambridge Crystallographic Data Centre, Cambridge, UK
- SuRE Pharma Consulting, Zionsville, IN, USA
| | - Sandrine Rome
- UCB Pharma SA, Chemin du Foriest, Braine-l'Alleud, Belgium
| | - Ahmad Y Sheikh
- Solid State Chemistry, Research & Development, AbbVie, North Chicago, IL, USA
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, Luxembourg City, Luxembourg
| | | | | |
Collapse
|
5
|
Andrews J, Yufit DS, McCabe JF, Fox MA, Steed JW. Vapor Sorption and Halogen-Bond-Induced Solid-Form Rearrangement of a Porous Pharmaceutical. CRYSTAL GROWTH & DESIGN 2023; 23:2628-2633. [PMID: 37038401 PMCID: PMC10080649 DOI: 10.1021/acs.cgd.2c01464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/08/2023] [Indexed: 06/19/2023]
Abstract
A porous, nonsolvated polymorph of the voltage-gated sodium channel blocker mexiletine hydrochloride absorbs iodine vapor to give a pharmaceutical cocrystal incorporating an I2Cl- anion that forms a halogen-π interaction with the mexiletine cations. The most thermodynamically stable form of the compound does not absorb iodine. This example shows that vapor sorption is a potentially useful and underused tool for bringing about changes in pharmaceutical solid form as part of a solid form screening protocol.
Collapse
Affiliation(s)
- Jessica
L. Andrews
- Department
of Chemistry, Durham University, South Road, Durham DH1 3LE, U.K.
| | - Dmitry S. Yufit
- Department
of Chemistry, Durham University, South Road, Durham DH1 3LE, U.K.
| | - James F. McCabe
- Pharmaceutical
Sciences, R&D, AstraZeneca, Charter Way, Silk Road Business
Park, Macclesfield SK10
2NA, U.K.
| | - Mark A. Fox
- Department
of Chemistry, Durham University, South Road, Durham DH1 3LE, U.K.
| | - Jonathan W. Steed
- Department
of Chemistry, Durham University, South Road, Durham DH1 3LE, U.K.
| |
Collapse
|
6
|
Surov AO, Ramazanova AG, Voronin AP, Drozd KV, Churakov AV, Perlovich GL. Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals. Pharmaceutics 2023; 15:pharmaceutics15030836. [PMID: 36986697 PMCID: PMC10052035 DOI: 10.3390/pharmaceutics15030836] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
In this study, the existing set of carbamazepine (CBZ) cocrystals was extended through the successful combination of the drug with the positional isomers of acetamidobenzoic acid. The structural and energetic features of the CBZ cocrystals with 3- and 4-acetamidobenzoic acids were elucidated via single-crystal X-ray diffraction followed by QTAIMC analysis. The ability of three fundamentally different virtual screening methods to predict the correct cocrystallization outcome for CBZ was assessed based on the new experimental results obtained in this study and data available in the literature. It was found that the hydrogen bond propensity model performed the worst in distinguishing positive and negative results of CBZ cocrystallization experiments with 87 coformers, attaining an accuracy value lower than random guessing. The method that utilizes molecular electrostatic potential maps and the machine learning approach named CCGNet exhibited comparable results in terms of prediction metrics, albeit the latter resulted in superior specificity and overall accuracy while requiring no time-consuming DFT computations. In addition, formation thermodynamic parameters for the newly obtained CBZ cocrystals with 3- and 4-acetamidobenzoic acids were evaluated using temperature dependences of the cocrystallization Gibbs energy. The cocrystallization reactions between CBZ and the selected coformers were found to be enthalpy-driven, with entropy terms being statistically different from zero. The observed difference in dissolution behavior of the cocrystals in aqueous media was thought to be caused by variations in their thermodynamic stability.
Collapse
Affiliation(s)
- Artem O Surov
- G.A. Krestov Institute of Solution Chemistry RAS, 153045 Ivanovo, Russia
| | - Anna G Ramazanova
- G.A. Krestov Institute of Solution Chemistry RAS, 153045 Ivanovo, Russia
| | | | - Ksenia V Drozd
- G.A. Krestov Institute of Solution Chemistry RAS, 153045 Ivanovo, Russia
| | - Andrei V Churakov
- Institute of General and Inorganic Chemistry RAS, Leninsky Prosp. 31, 119991 Moscow, Russia
| | - German L Perlovich
- G.A. Krestov Institute of Solution Chemistry RAS, 153045 Ivanovo, Russia
| |
Collapse
|
7
|
Conformational Screening of Arbidol Solvates: Investigation via 2D NOESY. Pharmaceutics 2023; 15:pharmaceutics15010226. [PMID: 36678855 PMCID: PMC9865235 DOI: 10.3390/pharmaceutics15010226] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/31/2022] [Accepted: 01/07/2023] [Indexed: 01/12/2023] Open
Abstract
Understanding of the nucleation process's fundamental principles in saturated solutions is an urgent task. To do this task, it is necessary to control the formation of polymorphic forms of biologically active compounds. In certain cases, a compound can exist in a single polymorphic form, but have several solvates which can appear in different crystal forms, depending on the medium and conditions of formation, and show different pharmaceutical activity. In the present paper, we report on the analysis of Arbidol conformational preferences in two solvents of different polarities-deuterated chloroform and dimethyl sulfoxide-at 25 °C, using the 2D NOESY method. The Arbidol molecule has various solvate forms depending on the molecular conformation. The method based on the nuclear Overhauser effect spectroscopy was shown to be efficient in the analysis of complex heterocyclic compounds possessing conformation-dependent pseudo-polymorphism. It is one of the types of polymorphism observed in compounds forming crystal solvates. Combined use of NMR methods and X-ray data allowed determining of conformer populations of Arbidol in CDCl3 and DMSO-d6 which were found to be 8/92% and 37/63%, respectively. The preferred conformation in solution is the same that appears in stable crystal solvates of Arbidol.
Collapse
|
8
|
Deem MC, Cai I, Derasp JS, Prieto PL, Sato Y, Liu J, Kukor AJ, Hein JE. Best Practices for the Collection of Robust Time Course Reaction Profiles for Kinetic Studies. ACS Catal 2023. [DOI: 10.1021/acscatal.2c05045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Madeleine C. Deem
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Isabelle Cai
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Joshua S. Derasp
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Paloma L. Prieto
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Yusuke Sato
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Junliang Liu
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Andrew J. Kukor
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| | - Jason E. Hein
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
| |
Collapse
|
9
|
Liu Z, Ma H, Zhou L, Liu Y, Huang Z, Liao X, Zhao Y. DEM-DDM Investigation of the Tablet Coating Process Using Different Particle Shape Models. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c04030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Zihan Liu
- Institute of Process Equipment, Zhejiang University, Hangzhou310027, China
| | - Huaqing Ma
- Institute of Process Equipment, Zhejiang University, Hangzhou310027, China
| | - Lianyong Zhou
- Institute of Process Equipment, Zhejiang University, Hangzhou310027, China
| | - Yanlei Liu
- Hangzhou Special Equipment Inspection and Research Institute, Hangzhou310051, China
| | - Ze Huang
- Hangzhou Special Equipment Inspection and Research Institute, Hangzhou310051, China
| | - Xiaoling Liao
- Hangzhou Special Equipment Inspection and Research Institute, Hangzhou310051, China
| | - Yongzhi Zhao
- Institute of Process Equipment, Zhejiang University, Hangzhou310027, China
| |
Collapse
|
10
|
Ong JJ, Castro BM, Gaisford S, Cabalar P, Basit AW, Pérez G, Goyanes A. Accelerating 3D printing of pharmaceutical products using machine learning. Int J Pharm X 2022; 4:100120. [PMID: 35755603 PMCID: PMC9218223 DOI: 10.1016/j.ijpx.2022.100120] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/26/2022] [Accepted: 05/29/2022] [Indexed: 12/11/2022] Open
Abstract
Three-dimensional printing (3DP) has seen growing interest within the healthcare industry for its ability to fabricate personalized medicines and medical devices. However, it may be burdened by the lengthy empirical process of formulation development. Active research in pharmaceutical 3DP has led to a wealth of data that machine learning could utilize to provide predictions of formulation outcomes. A balanced dataset is critical for optimal predictive performance of machine learning (ML) models, but data available from published literature often only include positive results. In this study, in-house and literature-mined data on hot melt extrusion (HME) and fused deposition modeling (FDM) 3DP formulations were combined to give a more balanced dataset of 1594 formulations. The optimized ML models predicted the printability and filament mechanical characteristics with an accuracy of 84%, and predicted HME and FDM processing temperatures with a mean absolute error of 5.5 °C and 8.4 °C, respectively. The performance of these ML models was better than previous iterations with a smaller and a more imbalanced dataset, highlighting the importance of providing a structured and heterogeneous dataset for optimal ML performance. The optimized models were integrated in an updated web-application, M3DISEEN, that provides predictions on filament characteristics, printability, HME and FDM processing temperatures, and drug release profiles (https://m3diseen.com/predictionsFDM/). By simulating the workflow of preparing FDM-printed pharmaceutical products, the web-application expedites the otherwise empirical process of formulation development, facilitating higher pharmaceutical 3DP research throughput.
Collapse
Affiliation(s)
- Jun Jie Ong
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Brais Muñiz Castro
- IRLab, CITIC Research Center, Department of Computer Science, University of A Coruña, Spain
| | - Simon Gaisford
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK.,FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK
| | - Pedro Cabalar
- IRLab, Department of Computer Science, University of A Coruña, Spain
| | - Abdul W Basit
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK.,FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK
| | - Gilberto Pérez
- IRLab, CITIC Research Center, Department of Computer Science, University of A Coruña, Spain
| | - Alvaro Goyanes
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK.,FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK.,Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, iMATUS and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782, Spain
| |
Collapse
|
11
|
Soares TA, Nunes-Alves A, Mazzolari A, Ruggiu F, Wei GW, Merz K. The (Re)-Evolution of Quantitative Structure-Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning Methods. J Chem Inf Model 2022; 62:5317-5320. [PMID: 36437763 DOI: 10.1021/acs.jcim.2c01422] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Thereza A Soares
- Department of Chemistry, University of São Paulo, Ribeirão Preto 055508-090, Brazil.,Hylleraas Centre for Quantum Molecular Sciences, University of Oslo, Oslo 0315, Norway
| | - Ariane Nunes-Alves
- Institute of Chemistry, Technische Universität Berlin, Berlin 10623, Germany
| | - Angelica Mazzolari
- Department of Pharmaceutical Sciences, University of Milan, Via Mangiagalli 25, Milan I-20133, Italy
| | - Fiorella Ruggiu
- Insitro Inc., 279 East Grand Avenue, South San Francisco 94080, California, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing 48824, Michigan, United States
| | - Kenneth Merz
- Department of Chemistry, Michigan State University, East Lansing 48824, Michigan, United States
| |
Collapse
|