1
|
Srivathsa AV, Sadashivappa NM, Hegde AK, Radha S, Mahesh AR, Ammunje DN, Sen D, Theivendren P, Govindaraj S, Kunjiappan S, Pavadai P. A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery. Curr Pharm Des 2023; 29:1180-1192. [PMID: 37132148 DOI: 10.2174/1381612829666230428110542] [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: 11/30/2022] [Revised: 02/06/2023] [Accepted: 02/27/2023] [Indexed: 05/04/2023]
Abstract
Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain's thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this.
Collapse
Affiliation(s)
- Anjana Vidya Srivathsa
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Nandini Markuli Sadashivappa
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Apeksha Krishnamurthy Hegde
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Srimathi Radha
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, Faculty of Medicine and Health Sciences, SRM Institute of Science and Technology, Chengalpattu District, Kattankulathur, Tamil Nadu, 603203, India
| | - Agasa Ramu Mahesh
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Damodar Nayak Ammunje
- Department of Pharmacology, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Debanjan Sen
- Department of Pharmaceutical Chemistry, BCDA College of Pharmacy & Technology, Hridaypur, Kolkata, 700127, West Bengal, India
| | - Panneerselvam Theivendren
- Department of Pharmaceutical Chemistry, Swamy Vivekanandha College of Pharmacy, Elayampalayam, Tiruchengode, 637205, India
| | - Saravanan Govindaraj
- Department of Pharmaceutical Chemistry, MNR College of Pharmacy, Fasalwadi, Sangareddy, 502 001, India
| | - Selvaraj Kunjiappan
- Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India
| | - Parasuraman Pavadai
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| |
Collapse
|
2
|
Mouchlis VD, Afantitis A, Serra A, Fratello M, Papadiamantis AG, Aidinis V, Lynch I, Greco D, Melagraki G. Advances in de Novo Drug Design: From Conventional to Machine Learning Methods. Int J Mol Sci 2021; 22:1676. [PMID: 33562347 PMCID: PMC7915729 DOI: 10.3390/ijms22041676] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/31/2021] [Accepted: 01/31/2021] [Indexed: 12/11/2022] Open
Abstract
. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development.
Collapse
Affiliation(s)
| | - Antreas Afantitis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus;
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (M.F.); (D.G.)
- BioMEdiTech Institute, Tampere University, 33520 Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (M.F.); (D.G.)
- BioMEdiTech Institute, Tampere University, 33520 Tampere, Finland
| | - Anastasios G. Papadiamantis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus;
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Vassilis Aidinis
- Institute for Bioinnovation, Biomedical Sciences Research Center Alexander Fleming, Fleming 34, 16672 Athens, Greece;
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (M.F.); (D.G.)
- BioMEdiTech Institute, Tampere University, 33520 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
- Finnish Center for Alternative Methods (FICAM), Tampere University, 33520 Tampere, Finland
| | - Georgia Melagraki
- Division of Physical Sciences & Applications, Hellenic Military Academy, 16672 Vari, Greece
| |
Collapse
|
3
|
Seidel T, Schuetz DA, Garon A, Langer T. The Pharmacophore Concept and Its Applications in Computer-Aided Drug Design. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 2019; 110:99-141. [PMID: 31621012 DOI: 10.1007/978-3-030-14632-0_4] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Pharmacophore-based techniques currently are an integral part of many computer-aided drug design workflows and have been successfully and extensively applied for tasks such as virtual screening, de novo design, and lead optimization. Pharmacophore models can be derived both in a receptor-based and in a ligand-based manner, and provide an abstract description of essential non-bonded interactions that typically occur between small-molecule ligands and macromolecular targets. Due to their simplistic and abstract nature, pharmacophores are both perfectly suited for efficient computer processing and easy to comprehend by life and physical scientists. As a consequence, they have also proven to be a valuable tool for communicating between computational and medicinal chemists.This chapter aims to provide a short overview of the pharmacophore concept and its applications in modern computer-aided drug design. The chapter is divided into three distinct parts. The first section contains a brief introduction to the pharmacophore concept. The second section provides a description of the most common nonbonded interaction types and their representation as pharmacophoric features. Furthermore, it gives an overview of the various methods for pharmacophore generation and important pharmacophore-based techniques in drug design. This part concludes with examples for recent pharmacophore concept-related research and development. The last section is dedicated to a review of research in the field of natural product chemistry as carried out by employing pharmacophore-based drug design methods.
Collapse
Affiliation(s)
- Thomas Seidel
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria.
| | - Doris A Schuetz
- InteLigand GmbH, IRIC-Institut de Recherche en Immunologie et en Cancérologie, Université de Montréal, Montréal, QC, Canada
| | - Arthur Garon
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| |
Collapse
|
4
|
Suryanarayanan V, Panwar U, Chandra I, Singh SK. De Novo Design of Ligands Using Computational Methods. Methods Mol Biol 2018; 1762:71-86. [PMID: 29594768 DOI: 10.1007/978-1-4939-7756-7_5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
De novo design technique is complementary to high-throughput virtual screening and is believed to contribute in pharmaceutical development of novel drugs with desired properties at a very low cost and time-efficient manner. In this chapter, we outline the basic de novo design concepts based on computational methods with an example.
Collapse
Affiliation(s)
- Venkatesan Suryanarayanan
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Umesh Panwar
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Ishwar Chandra
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India.
| |
Collapse
|
5
|
Abstract
Cyclin-dependent kinases (CDKs) are core components of the cell cycle machinery that govern the transition between phases during cell cycle progression. Abnormalities in CDKs activity and regulation are common features of cancer, making CDK family members attractive targets for the development of anticancer drugs. One of the main bottlenecks hampering the development of drugs for kinase is the difficulty to attain selectivity. A huge variety of small molecules have been reported as CDK inhibitors, as potential anticancer agents, but none of these has been approved for commercial use. Computer-based molecular design supports drug discovery by suggesting novel new chemotypes and compound modifications for lead candidate optimization. One of the methods known as de novo ligand design technique has emerged as a complementary approach to high-throughput screening. Several automated de novo software programs have been written, which automatically design novel structures to perfectly fit in known binding site. The de novo design supports drug discovery assignments by generating novel pharmaceutically active agents with desired properties in a cost as well as time efficient approach. This chapter describes procedure and an overview of computer-based molecular de novo design methods on a conceptual level with successful examples of CDKs inhibitors.
Collapse
|
6
|
Abstract
Structure-based drug design has become an essential tool for rapid lead discovery and optimization. As available structural information has increased, researchers have become increasingly aware of the importance of protein flexibility for accurate description of the native state. Typical protein-ligand docking efforts still rely on a single rigid receptor, which is an incomplete representation of potential binding conformations of the protein. These rigid docking efforts typically show the best performance rates between 50 and 75%, while fully flexible docking methods can enhance pose prediction up to 80-95%. This review examines the current toolbox for flexible protein-ligand docking and receptor surface mapping. Present limitations and possibilities for future development are discussed.
Collapse
Affiliation(s)
- Katrina W. Lexa
- Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, MI 48109-1065, USA
| | - Heather A. Carlson
- Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, MI 48109-1065, USA
| |
Collapse
|
7
|
Reymond JL, Ruddigkeit L, Blum L, van Deursen R. The enumeration of chemical space. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2012. [DOI: 10.1002/wcms.1104] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
|
8
|
Hartenfeller M, Schneider G. Enabling future drug discovery by
de novo
design. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2011. [DOI: 10.1002/wcms.49] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Markus Hartenfeller
- Computer‐Assisted Drug Design, Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland
| | - Gisbert Schneider
- Computer‐Assisted Drug Design, Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland
| |
Collapse
|
9
|
Kutchukian PS, Shakhnovich EI. De novo design: balancing novelty and confined chemical space. Expert Opin Drug Discov 2010; 5:789-812. [PMID: 22827800 DOI: 10.1517/17460441.2010.497534] [Citation(s) in RCA: 62] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
IMPORTANCE OF THE FIELD De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner. AREAS COVERED IN THIS REVIEW This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation. WHAT THE READER WILL GAIN The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future. TAKE HOME MESSAGE De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.
Collapse
Affiliation(s)
- Peter S Kutchukian
- Harvard University, Chemistry and Chemical Biology Department, 12 Oxford Street, Cambridge, MA 02138, USA
| | | |
Collapse
|
10
|
Huang Q, Li LL, Yang SY. PhDD: a new pharmacophore-based de novo design method of drug-like molecules combined with assessment of synthetic accessibility. J Mol Graph Model 2010; 28:775-87. [PMID: 20206562 DOI: 10.1016/j.jmgm.2010.02.002] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2009] [Revised: 01/25/2010] [Accepted: 02/07/2010] [Indexed: 02/05/2023]
Abstract
This account describes a new pharmacophore-based de novo design method of drug-like molecules (PhDD). The method PhDD first generates a set of new molecules that completely conform to the requirements of a given pharmacophore model, followed by a series of assessments to the generated molecules, including assessments of drug-likeness, bioactivity, and synthetic accessibility. PhDD is tested on three typical examples, namely, pharmacophore hypotheses of histone deacetylase (HDAC), cyclin-dependent kinase 2 (CDK2) and HIV-1 integrase (IN) inhibitors. The test results demonstrate that PhDD is able to generate molecules with novel structures but having similar biological functions with existing inhibitors. The validity of PhDD together with its ability of assessing synthetic accessibility makes it a useful tool in rational drug design.
Collapse
Affiliation(s)
- Qi Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, #1 Keyuan Road 4, Chengdu, Sichuan 610041, China
| | | | | |
Collapse
|
11
|
Fan H, Irwin JJ, Webb BM, Klebe G, Shoichet BK, Sali A. Molecular docking screens using comparative models of proteins. J Chem Inf Model 2009; 49:2512-27. [PMID: 19845314 PMCID: PMC2790034 DOI: 10.1021/ci9003706] [Citation(s) in RCA: 109] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Two orders of magnitude more protein sequences can be modeled by comparative modeling than have been determined by X-ray crystallography and NMR spectroscopy. Investigators have nevertheless been cautious about using comparative models for ligand discovery because of concerns about model errors. We suggest how to exploit comparative models for molecular screens, based on docking against a wide range of crystallographic structures and comparative models with known ligands. To account for the variation in the ligand-binding pocket as it binds different ligands, we calculate "consensus" enrichment by ranking each library compound by its best docking score against all available comparative models and/or modeling templates. For the majority of the targets, the consensus enrichment for multiple models was better than or comparable to that of the holo and apo X-ray structures. Even for single models, the models are significantly more enriching than the template structure if the template is paralogous and shares more than 25% sequence identity with the target.
Collapse
Affiliation(s)
- Hao Fan
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, San Francisco, California 94158, USA
| | | | | | | | | | | |
Collapse
|
12
|
Mahmoud KA, Hrapovic S, Luong JHT. Picomolar detection of protease using peptide/single walled carbon nanotube/gold nanoparticle-modified electrode. ACS NANO 2008; 2:1051-1057. [PMID: 19206503 DOI: 10.1021/nn8000774] [Citation(s) in RCA: 71] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Picomolar electrochemical detection of human immunodeficiency virus type-1 protease (HIV-1 PR) using ferrocene (Fc)-pepstatin-modified surfaces has been presented. Gold electrode surface was modified with gold nanoparticles (AuNP) or thiolated single walled carbon nanotubes/gold nanoparticles (SWCNT/AuNP). Thiol-terminated Fc-pepstatin was then self-assembled on such surfaces as confirmed by Raman spectroscopy and scanning electron microscope. The interaction between the Fc-pepstatin-modified substrates and HIV-1 PR was studied by cyclic voltammetry and electrochemical impedance spectroscopy. Both electrode materials showed enhanced electrochemical responses to increasing concentrations of HIV-1 PR with shifting to higher potentials as well as decrease in the overall signal intensity. However, the sensing electrode modified with thiolated SWCNTs/AuNPs showed remarkable detection sensitivity with an estimated detection limit of 0.8 pM.
Collapse
Affiliation(s)
- Khaled A Mahmoud
- Biotechnology Research Institute, National Research Council Canada, Montreal, Canada H4P2R2
| | | | | |
Collapse
|
13
|
Zhu J, Alexov E, Honig B. Comparative study of generalized born models: Born radii and peptide folding. J Phys Chem B 2007; 109:3008-22. [PMID: 16851315 DOI: 10.1021/jp046307s] [Citation(s) in RCA: 73] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In this study, we have implemented four analytical generalized Born (GB) models and investigated their performance in conjunction with the GROMOS96 force field. The four models include that of Still and co-workers, the HCT model of Cramer, Truhlar, and co-workers, a modified form of the AGB model of Levy and co-workers, and the GBMV2 model of Brooks and co-workers. The models were coded independently and implemented in the GROMOS software package and in TINKER. They were compared in terms of their ability to reproduce the results of Poisson-Boltzmann (PB) calculations and in their performance in the ab initio peptide folding of two peptides, one that forms a beta-hairpin in solution and one that forms an alpha-helix. In agreement with previous work, the GBMV2 model is most successful in reproducing PB results while the other models tend to underestimate the effective Born radii of buried atoms. In contrast, stochastic dynamics simulations on the folding of the two peptides, the C-terminus beta-hairpin of the B1 domain of protein G and the alanine-based alpha-helical peptide 3K(I), suggest that the simpler GB models are more effective in sampling conformational space. Indeed, the Still model used in conjunction with the GROMOS96 force field is able to fold the hairpin peptide to a native-like structure without the benefit of enhanced sampling techniques. This is due in part to the properties of the united-atom GROMOS96 force field which appears to be more flexible, and hence to sample more efficiently, than force fields such as OPLSAA. Our results suggest a general strategy which involves using different combinations of force fields and solvent models in different applications, for example, using GROMOS96 and a simple GB model in sampling and OPLSAA and a more accurate GB model in refinement. The fact that various methods have been implemented in a unified way should facilitate the testing and subsequent use of different methods to evaluate conformational free energies in different applications. Our results also bear on some general issues involved in peptide folding and structure prediction which are addressed in the Discussion.
Collapse
Affiliation(s)
- Jiang Zhu
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, New York 10032, USA
| | | | | |
Collapse
|
14
|
van Gunsteren WF, Bakowies D, Baron R, Chandrasekhar I, Christen M, Daura X, Gee P, Geerke DP, Glättli A, Hünenberger PH, Kastenholz MA, Oostenbrink C, Schenk M, Trzesniak D, van der Vegt NFA, Yu HB. Biomolecular modeling: Goals, problems, perspectives. Angew Chem Int Ed Engl 2007; 45:4064-92. [PMID: 16761306 DOI: 10.1002/anie.200502655] [Citation(s) in RCA: 408] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Computation based on molecular models is playing an increasingly important role in biology, biological chemistry, and biophysics. Since only a very limited number of properties of biomolecular systems is actually accessible to measurement by experimental means, computer simulation can complement experiment by providing not only averages, but also distributions and time series of any definable quantity, for example, conformational distributions or interactions between parts of systems. Present day biomolecular modeling is limited in its application by four main problems: 1) the force-field problem, 2) the search (sampling) problem, 3) the ensemble (sampling) problem, and 4) the experimental problem. These four problems are discussed and illustrated by practical examples. Perspectives are also outlined for pushing forward the limitations of biomolecular modeling.
Collapse
Affiliation(s)
- Wilfred F van Gunsteren
- Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, 8093 Zurich, Switzerland.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
15
|
Dean PM, Firth-Clark S, Harris W, Kirton SB, Todorov NP. SkelGen: a general tool for structure-basedde novoligand design. Expert Opin Drug Discov 2006; 1:179-89. [DOI: 10.1517/17460441.1.2.179] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
16
|
van Gunsteren WF, Bakowies D, Baron R, Chandrasekhar I, Christen M, Daura X, Gee P, Geerke DP, Glättli A, Hünenberger PH, Kastenholz MA, Oostenbrink C, Schenk M, Trzesniak D, van der Vegt NFA, Yu HB. Biomolekulare Modellierung: Ziele, Probleme, Perspektiven. Angew Chem Int Ed Engl 2006. [DOI: 10.1002/ange.200502655] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
17
|
Schneider G, Fechner U. Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 2005; 4:649-63. [PMID: 16056391 DOI: 10.1038/nrd1799] [Citation(s) in RCA: 538] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Ever since the first automated de novo design techniques were conceived only 15 years ago, the computer-based design of hit and lead structure candidates has emerged as a complementary approach to high-throughput screening. Although many challenges remain, de novo design supports drug discovery projects by generating novel pharmaceutically active agents with desired properties in a cost- and time-efficient manner. In this review, we outline the various design concepts and highlight current developments in computer-based de novo design.
Collapse
Affiliation(s)
- Gisbert Schneider
- Johann Wolfgang Goethe-University, Institute of Organic Chemistry and Chemical Biology, Marie-Curie-Str. 11 D-60439 Frankfurt am Main, Germany.
| | | |
Collapse
|
18
|
Jenwitheesuk E, Samudrala R. Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations. BMC STRUCTURAL BIOLOGY 2003; 3:2. [PMID: 12675950 PMCID: PMC154089 DOI: 10.1186/1472-6807-3-2] [Citation(s) in RCA: 54] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2002] [Accepted: 04/01/2003] [Indexed: 11/23/2022]
Abstract
BACKGROUND The accurate prediction of enzyme-substrate interaction energies is one of the major challenges in computational biology. This study describes the improvement of protein-ligand binding energy prediction by incorporating protein flexibility through the use of molecular dynamics (MD) simulations. RESULTS Docking experiments were undertaken using the program AutoDock for twenty-five HIV-1 protease-inhibitor complexes determined by x-ray crystallography. Protein-rigid docking without any dynamics produced a low correlation of 0.38 between the experimental and calculated binding energies. Correlations improved significantly for all time scales of MD simulations of the receptor-ligand complex. The highest correlation coefficient of 0.87 between the experimental and calculated energies was obtained after 0.1 picoseconds of dynamics simulation. CONCLUSION Our results indicate that relaxation of protein complexes by MD simulation is useful and necessary to obtain binding energies that are representative of the experimentally determined values.
Collapse
Affiliation(s)
- Ekachai Jenwitheesuk
- Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA
- Department of Clinical Microbiology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Ram Samudrala
- Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA
| |
Collapse
|
19
|
Koo JC, Lam JS, Chass GA, Torday LL, Varro A, Papp JG. Conformational dependence of the intrinsic acidity of the aspartic acid residue sidechain in N-acetyl-l-aspartic acid-N′-methylamide. ACTA ACUST UNITED AC 2003. [DOI: 10.1016/s0166-1280(02)00639-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
20
|
How reliable could economic Hartree–Fock computations be in studying large, folded peptides? A comparative HF and DFT case study on N- and C-protected aspartic acid. ACTA ACUST UNITED AC 2002. [DOI: 10.1016/s0166-1280(02)00579-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|