Submitted Successfully!
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Ver. Summary Created by Modification Content Size Created at Operation
1 -- 2324 2022-11-24 07:23:31 |
2 format correction -6 word(s) 2318 2022-11-25 03:46:29 | |
3 format correction Meta information modification 2318 2022-11-28 09:07:06 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Zhang, Y.;  Luo, M.;  Wu, P.;  Wu, S.;  Lee, T.;  Bai, C. Computational Biology in Drug Design. Encyclopedia. Available online: (accessed on 05 December 2023).
Zhang Y,  Luo M,  Wu P,  Wu S,  Lee T,  Bai C. Computational Biology in Drug Design. Encyclopedia. Available at: Accessed December 05, 2023.
Zhang, Yue, Mengqi Luo, Peng Wu, Song Wu, Tzong-Yi Lee, Chen Bai. "Computational Biology in Drug Design" Encyclopedia, (accessed December 05, 2023).
Zhang, Y.,  Luo, M.,  Wu, P.,  Wu, S.,  Lee, T., & Bai, C.(2022, November 24). Computational Biology in Drug Design. In Encyclopedia.
Zhang, Yue, et al. "Computational Biology in Drug Design." Encyclopedia. Web. 24 November, 2022.
Computational Biology in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, researchers propose a methodology for integrating various computational techniques into new drug discovery and design.

computational biology computer-aided drug design (CADD) artificial intelligence-aided drug design (AIDD)

1. Application of Molecular Mechanics in Drug Design

Molecular mechanics (MM) is an approach which approximately treats the molecules with the laws of classical mechanics and saves the computational resources required for quantum mechanical calculations [1]. Over the past decades, MM approach plays an important role in understanding the ligand-protein structures, interactions and optimizing leads. It is achieved by MM potential energy function, which represents the sum of different energy terms, referred as “force fields” [2]. MM potential energy functions are used in various sampling methods, such as MD and MC (Monte Carlo). MD simulations are often utilized in drug discovery [3][4]. MD is one of the most popular algorithms for sampling. It utilizes various integration algorithms, such as Verlet’s Algorithm, Leap-frog Algorithm and Beeman’s Algorithm, to interpret classical Newton’s equation of motion to analyze the trajectories, movements and interactions in a given molecular system [4]. Time-dependent properties can be obtained from MD [5]. The system is generally a biomacromolecule, such as a protein for example an enzyme, with a solvent environment. For this protein or enzyme system, the initial protein structure is resolved by experiments [6]. Then, the structure could be modelled by different methods. After that, simulations start with the prepared model. X-ray crystallography is used as an experimental method for obtaining the three-dimensional protein structure [7]. However, X-ray requires the protein to form stable crystals and the crystal quality determines the resolution of the structure, which limits the obtainment of high-quality protein structures, especially of membrane proteins [8]. Cryo-EM addresses the problem without the need to form crystals [9][10]. Cryo-EM can determine even quite unstable and intractable membrane protein structures [10]. However, Cryo-EM is not a panacea. In cryo-EM, sample quality is still the most critical factor for determining the high-resolution structure [11]. If no experimental structure is available, modeling or predicting the structure is necessary. Homology modeling [12][13] and AlphaFold developed by DeepMind [14][15][16] are preferred techniques for acquiring the initial protein structure. In the molecular dynamics simulation, atoms and molecules of the system interact during the fixed time, providing the dynamic features of the system. Atom trajectories are generally determined by Newton’s laws of motion. Molecular mechanics methods with various force fields [17][18][19][20][21][22] are employed to calculate the energies of the system.

1.1. Application in Investigating the Mechanism of the Target Protein

Target protein can be regulated by drugs to cure the disease or relieve the symptoms. The overall process is dynamic and usually accompanied by the conformational changes of the target protein. Target protein conformation has an essential role in drug design. Even minor changes, as well as the motions of residues, may affect the target–ligand interactions. MD simulations can provide dynamic information about the target protein and the ligand in terms of drug design, which cannot be obtained through experimental methods. Compared with experiments, MD simulations can provide detailed information about the target protein folding process and describe the conformational changes of the protein with environmental changes such as temperature, pH, and residue mutations, with detailed energetic information. At present, MD simulations have been broadly applied to study the molecular mechanisms of the target protein to aid drug design.
For example, Horikoshi et al. revealed the molecular mechanisms underlying the loss of activity in the most severe glucose-6-phosphate dehydrogenase (G6PD) deficiency [23]. It is triggered by the long-distance propagation of structural defects at the dimer interface. The findings indicated that a promising drug can possibly be discovered and developed by correcting these structural defects. While studying pathogenic mutations in the kinesin-3 motor KIF1A by using MD simulations, Budaitis et al. found that these mutations were linked to neurodevelopmental and neurodegenerative disorders that impaired neck linker docking and dramatically reduced the KIF1A force generation [24]. Zanetti-Domingues’s work revealed autoinhibition mechanisms in dimers and oligomers of the epidermal growth factor receptor (EGFR) and supported that dysregulated species bear populations of symmetric and asymmetric kinase dimers coexisting in an equilibrium [25]. The structural feature of the assembly inspires the related drug design. Based on MD simulations, Zhu’s lab elaborated on the genotype-determined EGFR-RTK heterodimerization and its drug resistance mechanism in lung cancer caused by a tighter EGFR-RTK crosstalk [26]. The study promotes drug design against the tighter crosstalk of the genotype determined. Understanding the dynamic behaviors of sirtuins, which have several ligand-binding sites [27], may provide perspectives for the design of selective inhibitors or activators. Polymyxin resistance was found to be induced by lipopolysaccharides and outer membrane vesicles in the multidrug-resistant Pseudomonas aeruginosa [28]. Based on this mechanism, an intelligent strategy for designing lipopeptide antibiotics against polymyxin resistance was developed [28]. The strategy may be suitable for the discovery of other classes of bacterial pathogen-targeting antibiotics. In addition to regular MM approach, coarse-grained models can be used to investigate the molecular mechanism of the target.

1.2. Application in Molecular Docking

In molecular docking, according to the space complementarity and energy match, compounds are docked in the specific site. Then, the docking poses are scored and ranked based on the scores [29]. On the basis of molecular docking, VS has been indispensable to drug discovery [30][31]. VS saves time and costs for drug discovery and efficiently obtains various molecule scaffolds [32][33][34][35][36]. The complete VS process consists of pre-processing compound libraries, molecular docking, and the selection of pretest compounds [37][38][39]. In general, the enrichment factor greatly determines the success of VS. The enrichment factor is a validation tool that evaluates the effectiveness of VS by computing the ratio of active molecules among the tested molecules in the initial library. For each VS step, different strategies are used to enhance the enrichment factor [40][41]. The VS results depend on the rationality of the docking poses between the target and ligand, and the accuracy of binding affinity [42][43][44][45][46][47][48][49][50].
After VS step, filtering promising candidates may then be sampled with MD simulations. The use of MD simulations can improve the flexibility in conformational sampling, which increases the number of degrees of freedom of the system and consequently in the computational effort [51]. For MM MD simulations, one of the most time-consuming parts is the calculation of the interactions between each atom in the system, which cost more than 90% of the total simulation time. This is mainly related to the calculation giving rise to O(N2) computational complexity (N represents the number of atoms in the system) [52][53][54]. The cutoff method applied to treat the interactions between atoms can reduce the computational complexity to O(N) [52][53]. Compared with the three-dimensional structure of the target protein obtained through X-ray or Cryo-EM, MD simulations take the flexibility of the target protein into account. The experimental structure is in the specific crystalized condition, which is possibly different from the real binding conformation with the ligand. A set of conformations can be obtained by modeling and simulations, especially the crucial intermediate or transition state that may contribute to the ligand–target protein binding process. MD simulations used to sample the specific conformation can provide more rational docking poses and achieve a higher enrichment factor. In addition to the conformation optimization of the target and ligand, MD simulations combined with binding free energy calculations are applied to assess the binding affinity of the ligand with the target. MM-PBSA and MM-GBSA are general approaches used to calculate binding free energy. Based on the trajectories from MD simulations, electrostatic energy, solvation energy, and van der Waals energy are calculated. Entropy change can be obtained through normal mode analysis. Then, the binding free energy can be obtained [55]. The binding free energy calculations are great and useful to augment the accuracy of the binding affinity of docking poses and improve the enrichment factor. But these high-cost sampling calculations are often used on an even smaller subset of potential hits.

1.3. Application in Lead Optimization

The optimal binding mode and the accurate binding affinity are vital for understanding the ligand–target interactions and guiding the modification of screened compounds. The ligand–target thermodynamical data, such as entropy change (ΔS) and free energy change (ΔG), can be determined through experiments and are used to distinguish between active and inactive compounds. However, the lack of details about target–ligand interactions limits further structural modifications of the compounds. MD simulation is a powerful approach for precisely evaluating the ligand–target binding modes. It can describe the detailed ligand–target interactions and determine the free energy contribution of each residue in the binding sites. The information can provide guidance for lead optimization.
Using the combination of MD simulations and VS, Patel’s lab optimized bedaquiline to decrease the severity of its adverse side effects and discovered that the compound CID 15947587 with low cardiotoxicity has the highest binding free energy (−85.27 kcal/mol) and an optimal docking score (−5.64) with mycobacterial ATP synthase enzyme [56]. Castillo’s group optimized AKT inhibitors by using MD simulations, thereby improving the binding affinity of the 2,4,6-trisubstituted pyridine scaffold in the ATP pocket of PKB/AKT and interacting well with glutamates/aspartates in ATP-binding sites [57]. Zhang et al. screened the new inhibitor against phosphodiesterase-2A (PDE2A). With the guidance of MD simulations, they obtained the optimized lead, LHB-8, forming an extra hydrogen bond with D808 and a hydrophobic interaction with T768, in addition to the interactions with Q859 and F862 of the PDE2A target [58].

1.4. Application of Coarse-Grained Models in Drug Design

All-atom MD simulations present the limitation while exploring the dynamic process of the large-scale target protein or long-time scale. Coarse-grained (CG) models help overcome the limitation well. When using CG models, the main chain of residues is in the all-atom state, but the side chain is a simplified united atom. Compared with all-atom MD simulations, CG simulations decrease the number of particles and make the potential energy surface smoother. Thus, the longer time and larger scale are available using CG models. Martini is a classical force filed to employ CG simulations. Martini is currently applied to study the mechanism and oligomerization of membrane proteins and self-assembly of proteins, predict conformational changes, and study binding and pore formation in membranes [59][60][61].
The CG model consistently developed by Warshel et al. [21][22][62] is advantageous in investigating the molecular mechanism of different biophysical systems, such as SARS-CoV-2 [63], GPCR [64], ATPase [65][66], and kinase [67]. This model can accurately describe the electrostatic term [68], which usually is the major contributor compared with other types of interactions in proteins. The CG profile can determine the dynamic information of the reaction in proteins, including the reaction energy barrier, rate-determining step, and the transition state. These results offer energetic details for understanding the working mechanism of proteins and guide rational drug discovery and development.

2. Application of QM in Drug Design

Structural studies have shown that the details of the potential drug target are valuable not only for lead discovery and optimization but also for the later steps of drug design, such as toxicity determination and prediction of binding modes of the leads and drug targets. During drug discovery, the molecular docking or pharmacophore model is used for predicting the binding modes in a short time. MD simulations can be employed to obtain flexible and rational docking modes. They can also guide drug design by exploring ligand–target interactions, such as studying the active site for extra electrostatic, hydrophilic, or hydrophobic interactions that can increase binding affinity [42][69][70]. Although MD simulations improve the accuracy of scoring and docking [71][72][73], concerns still exist, especially in enzymes or metal-containing drug targets, in which valence electron transfer occurs [74][75].
QM is considered the potential solution for the aforementioned concerns, which can explore drug target details at the electronic level [70][75][76]. At present, QM is increasingly applied to enzymes or metal-containing proteins that are considered drug targets, such as HIV-1 protease [77], human DHFR [78], and GPCR [79], and clarify the molecular mechanism for drug design [80][81][82][83]. QM is also used for designing novel drugs, including the high-affinity ligands of FKBP12 [84] and novel inhibitors of human DHFR [85].
Additionally, researchers have attempted to improve scoring by inducing QM approaches, especially QM-polarized ligand docking [86], and QMScore, a semiempirical QM (SQM) scoring function [87]. QM in combination with molecular mechanics (MM) has been developed to enhance the accuracy of docking and VS [75][88][89][90]. Fong et al. applied a series of QM/MM scoring functions to six HIV-1 proteases and found that parts of QM/MM functions were superior to MM functions [91]. Kim et al. [92] proposed a new strategy of using QM/MM with the implicit solvation model to rescore docking poses and improve the docking performance on 40 GPCR–ligand complexes. A significant improvement was observed over the conventional docking method. Chaskar et al. developed a QM/MM on-the-fly docking method to deal with polarization and metal interactions in docking and observed a significant improvement in scoring [93]. Compared to MD simulations, QM calculations are even more expensive. For example, the Hartree-Fock recovers approximately 99% of the total electronic energy and requires diagonalizing the M by M Fock matrix at O(M3) cost (M represents the number of basis functions) [94]. By Shor’s factoring algorithm, the complexity of quantum calculations is O((log2N)3) (N represents the number of atoms in the system) [95]. Moreover, QM calculations are restricted to systems of up to a few hundred atoms in contrast to MD simulations, which has evolved from simulating tens of thousands of atoms to handling over 100 million atoms comprising an entire cell organelle [96][97].


  1. Vanommeslaeghe, K.; Guvench, O. Molecular mechanics. Curr. Pharm. Des. 2014, 20, 3281–3292.
  2. Bekono, B.D.; Sona, A.N.; Eni, D.B.; Owono, L.C.; Megnassan, E.; Ntie-Kang, F. Molecular mechanics approaches for rational drug design: Forcefields and solvation models. Phys. Sci. Rev. 2021, 20190128.
  3. Williams-Noonan, B.J.; Yuriev, E.; Chalmers, D.K. Free Energy Methods in Drug Design: Prospects of “Alchemical Perturbation” in Medicinal Chemistry. J. Med. Chem. 2018, 61, 638–649.
  4. Allen, M.P. Introduction to molecular dynamics simulation. Comput. Soft Matter: Synth. Polym. Proteins 2004, 23, 1–28.
  5. Moroy, G.; Sperandio, O.; Rielland, S.; Khemka, S.; Druart, K.; Goyal, D.; Perahia, D.; Miteva, M.A. Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis. Future Med. Chem. 2015, 7, 2317–2331.
  6. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res. 2000, 28, 235–242.
  7. Whittig, L.; Allardice, W. X-ray diffraction techniques. Meth. Soil Anal. Part 1 Phys. Mineral. Meth. 1986, 5, 331–362.
  8. Javier, G.N.; Christopher, G.T. Cryo-Electron Microscopy: Moving Beyond X-Ray Crystal Structures for Drug Receptors and Drug Development. Ann. Rev. Pharmacol. Toxicol. 2020, 60, 51–71.
  9. Bai, X.-C.; McMullan, G.; Scheres, S.H. How cryo-EM is revolutionizing structural biology. Trends Biochem. Sci. 2015, 40, 49–57.
  10. Weissenberger, G.; Henderikx, R.J.M.; Peters, P.J. Understanding the invisible hands of sample preparation for cryo-EM. Nat. Methods 2021, 18, 463–471.
  11. Xu, D.; Zhou, Y.; Xie, D.; Guo, H. Antibiotic Binding to Monozinc CphA β-Lactamase from Aeromonas hydropila: Quantum Mechanical/Molecular Mechanical and Density Functional Theory Studies. J. Med. Chem. 2005, 48, 6679–6689.
  12. Krieger, E.; Nabuurs, S.B.; Vriend, G. Homology modeling. Meth. Biochem. Anal. 2003, 44, 509–524.
  13. Cavasotto, C.N.; Phatak, S.S. Homology modeling in drug discovery: Current trends and applications. Drug Discov. Today 2009, 14, 676–683.
  14. AlQuraishi, M. AlphaFold at CASP13. Bioinformatics 2019, 35, 4862–4865.
  15. Varadi, M.; Anyango, S.; Deshpande, M.; Nair, S.; Natassia, C.; Yordanova, G.; Yuan, D.; Stroe, O.; Wood, G.; Laydon, A. AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022, 50, D439–D444.
  16. Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021, 596, 583–589.
  17. MacKerell, A.D., Jr. Atomistic models and force fields. In Computational Biochemistry and Biophysics; CRC Press: Boca Raton, FL, USA, 2001; pp. 19–50.
  18. Robertson, M.J.; Tirado-Rives, J.; Jorgensen, W.L. Improved peptide and protein torsional energetics with the OPLS-AA force field. J. Chem. Theory Comput. 2015, 11, 3499–3509.
  19. Senftle, T.P.; Hong, S.; Islam, M.; Kylasa, S.B.; Zheng, Y.; Shin, Y.K.; Junkermeier, C.; Engel-Herbert, R.; Janik, M.J.; Aktulga, H.M. The ReaxFF reactive force-field: Development, applications and future directions. NPJ Comput. Mater. 2016, 2, 15011.
  20. Dauber-Osguthorpe, P.; Hagler, A.T. Biomolecular force fields: Where have we been, where are we now, where do we need to go and how do we get there? J. Comput. Aided Mol. Des. 2019, 33, 133–203.
  21. Lee, M.; Kolev, V.; Warshel, A. Validating a Coarse-Grained Voltage Activation Model by Comparing Its Performance to the Results of Monte Carlo Simulations. J. Phys. Chem. B 2017, 121, 11284–11291.
  22. Vorobyov, I.; Kim, I.; Chu, Z.T.; Warshel, A. Refining the treatment of membrane proteins by coarse-grained models. Proteins: Struct. Funct. Bioinform. 2016, 84, 92–117.
  23. Horikoshi, N.; Hwang, S.; Gati, C.; Matsui, T.; Castillo-Orellana, C.; Raub, A.G.; Garcia, A.A.; Jabbarpour, F.; Batyuk, A.; Broweleit, J. Long-range structural defects by pathogenic mutations in most severe glucose-6-phosphate dehydrogenase deficiency. Proc. Natl. Acad. Sci. USA 2021, 118, e2022790118.
  24. Budaitis, B.G.; Jariwala, S.; Rao, L.; Yue, Y.; Sept, D.; Verhey, K.J.; Gennerich, A. Pathogenic mutations in the kinesin-3 motor KIF1A diminish force generation and movement through allosteric mechanisms. J. Cell Biol. 2021, 220, e202004227.
  25. Zanetti-Domingues, L.C.; Korovesis, D.; Needham, S.R.; Tynan, C.J.; Sagawa, S.; Roberts, S.K.; Kuzmanic, A.; Ortiz-Zapater, E.; Jain, P.; Roovers, R.C. The architecture of EGFR’s basal complexes reveals autoinhibition mechanisms in dimers and oligomers. Nat. Commun. 2018, 9, 4325.
  26. Zhu, M.; Wang, D.D.; Yan, H. Genotype-determined EGFR-RTK heterodimerization and its effects on drug resistance in lung Cancer treatment revealed by molecular dynamics simulations. BMC Mol. Cell Biol. 2021, 22, 34.
  27. Rahnasto-Rilla, M.; Tyni, J.; Huovinen, M.; Jarho, E.; Kulikowicz, T.; Ravichandran, S.; Bohr, V.A.; Ferrucci, L.; Lahtela-Kakkonen, M.; Moaddel, R. Natural polyphenols as sirtuin 6 modulators. Sci. Rep. 2018, 8, 4163.
  28. Jiang, X.; Han, M.; Tran, K.; Patil, N.A.; Ma, W.; Roberts, K.D.; Xiao, M.; Sommer, B.; Schreiber, F.; Wang, L. An Intelligent Strategy with All-Atom Molecular Dynamics Simulations for the Design of Lipopeptides against Multidrug-Resistant Pseudomonas aeruginosa. J. Med. Chem. 2022, 65, 10001–10013.
  29. Cavasotto, C.; Orry, A.J.W. Ligand docking and structure-based virtual screening in drug discovery. Curr. Top. Med. Chem. 2007, 7, 1006–1014.
  30. Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discov. 2004, 3, 935–949.
  31. Meng, X.Y.; Zhang, H.X.; Mezei, M.; Cui, M. Molecular docking: A powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Des. 2011, 7, 146–157.
  32. Shoichet, B.K. Virtual screening of chemical libraries. Nature 2004, 432, 862–865.
  33. Irwin, J.J.; Shoichet, B.K. ZINC− a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005, 45, 177–182.
  34. Polgár, T.; Keseru, G.M. Integration of virtual and high throughput screening in lead discovery settings. Comb. Chem. High Throughput Screen. 2011, 14, 889–897.
  35. Hu, X.; Vujanac, M.; Southall, N.; Stebbins, C.E. Inhibitors of the Yersinia protein tyrosine phosphatase through high throughput and virtual screening approaches. Bioorganic Med. Chem. Lett. 2013, 23, 1056–1062.
  36. Lee, H.; Mittal, A.; Patel, K.; Gatuz, J.L.; Truong, L.; Torres, J.; Mulhearn, D.C.; Johnson, M.E. Identification of novel drug scaffolds for inhibition of SARS-CoV 3-Chymotrypsin-like protease using virtual and high-throughput screenings. Bioorganic Med. Chem. 2014, 22, 167–177.
  37. Lyne, P.D. Structure-based virtual screening: An overview. Drug Discov. Today 2002, 7, 1047–1055.
  38. Cheng, T.; Li, Q.; Zhou, Z.; Wang, Y.; Bryant, S.H. Structure-based virtual screening for drug discovery: A problem-centric review. AAPS J. 2012, 14, 133–141.
  39. Sastry, G.M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 2013, 27, 221–234.
  40. Gimeno, A.; Ojeda-Montes, M.J.; Tomás-Hernández, S.; Cereto-Massagué, A.; Beltrán-Debón, R.; Mulero, M.; Pujadas, G.; Garcia-Vallvé, S. The light and dark sides of virtual screening: What is there to know? Int. J. Mol. Sci. 2019, 20, 1375.
  41. Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W. Computational methods in drug discovery. Pharmacol. Rev. 2014, 66, 334–395.
  42. Liu, X.; Shi, D.; Zhou, S.; Liu, H.; Liu, H.; Yao, X. Molecular dynamics simulations and novel drug discovery. Expert Opin. Drug Discov. 2018, 13, 23–37.
  43. Taylor, R.; Jewsbury, P.; Essex, J. A review of protein-small molecule docking methods. J. Comput. Aided Mol. Des. 2002, 16, 151–166.
  44. Schulz-Gasch, T.; Stahl, M. Binding site characteristics in structure-based virtual screening: Evaluation of current docking tools. J. Mol. Model. 2003, 9, 47–57.
  45. Cross, J.B.; Thompson, D.C.; Rai, B.K.; Baber, J.C.; Fan, K.Y.; Hu, Y.; Humblet, C. Comparison of several molecular docking programs: Pose prediction and virtual screening accuracy. J. Chem. Inf. Model. 2009, 49, 1455–1474.
  46. Kumar, V.; Kancharla, S.; Jena, M.K. In silico virtual screening-based study of nutraceuticals predicts the therapeutic potentials of folic acid and its derivatives against COVID-19. VirusDisease 2021, 32, 29–37.
  47. Pantsar, T.; Poso, A. Binding affinity via docking: Fact and fiction. Molecules 2018, 23, 1899.
  48. Macip, G.; Garcia-Segura, P.; Mestres-Truyol, J.; Saldivar-Espinoza, B.; Ojeda-Montes, M.J.; Gimeno, A.; Cereto-Massagué, A.; Garcia-Vallvé, S.; Pujadas, G. Haste makes waste: A critical review of docking-based virtual screening in drug repurposing for SARS-CoV-2 main protease (M-pro) inhibition. Med. Res. Rev. 2022, 42, 744–769.
  49. Murugan, N.A.; Podobas, A.; Gadioli, D.; Vitali, E.; Palermo, G.; Markidis, S. A review on parallel virtual screening softwares for high-performance computers. Pharmaceuticals 2022, 15, 63.
  50. Rastelli, G.; Pinzi, L. Refinement and Rescoring of Virtual Screening Results. Front. Chem. 2019, 7, 498.
  51. Salmaso, V.; Moro, S. Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview. Front. Pharmacol. 2018, 9, 923.
  52. Liu, W.; Schmidt, B.; Voss, G.; Müller-Wittig, W. Molecular dynamics simulations on commodity GPUs with CUDA. In Proceedings of the International Conference on High-Performance Computing, Goa, India, 18–21 December 2007; pp. 185–196.
  53. Ramalingam, G.; Reps, T. On the computational complexity of dynamic graph problems. Theor. Comput. Sci. 1996, 158, 233–277.
  54. Salomon-Ferrer, R.; Gotz, A.W.; Poole, D.; Le Grand, S.; Walker, R.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J. Chem. Theory Comput. 2013, 9, 3878–3888.
  55. Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 2015, 10, 449–461.
  56. Ahmad, I.; Jadhav, H.; Shinde, Y.; Jagtap, V.; Girase, R.; Patel, H. Optimizing Bedaquiline for cardiotoxicity by structure based virtual screening, DFT analysis and molecular dynamic simulation studies to identify selective MDR-TB inhibitors. Silico Pharmacol. 2021, 9, 23.
  57. Sanabria-Chanaga, E.E.; Betancourt-Conde, I.; Hernández-Campos, A.; Téllez-Valencia, A.; Castillo, R. In silico hit optimization toward AKT inhibition: Fragment-based approach, molecular docking and molecular dynamics study. J. Biomol. Struct. Dyn. 2019, 37, 4301–4311.
  58. Zhang, C.; Feng, L.-J.; Huang, Y.; Wu, D.; Li, Z.; Zhou, Q.; Wu, Y.; Luo, H.B. Discovery of Novel Phosphodiesterase-2A Inhibitors by Structure-Based Virtual Screening, Structural Optimization, and Bioassay. J. Chem. Inf. Model. 2017, 57, 355–364.
  59. Ruskamo, S.; Yadav, R.P.; Sharma, S.; Lehtimäki, M.; Laulumaa, S.; Aggarwal, S.; Simons, M.; Bürck, J.; Ulrich, A.S.; Juffer, A.H. Atomic resolution view into the structure–function relationships of the human myelin peripheral membrane protein P2. Acta Crystallogr. Sect. D: Biol.Crystallogr. 2014, 70, 165–176.
  60. Kmiecik, S.; Gront, D.; Kolinski, M.; Wieteska, L.; Dawid, A.E.; Kolinski, A. Coarse-grained protein models and their applications. Chem. Rev. 2016, 116, 7898–7936.
  61. Singh, N.; Li, W. Recent advances in coarse-grained models for biomolecules and their applications. Int. J. Mol. Sci. 2019, 20, 3774.
  62. Vicatos, S.; Rychkova, A.; Mukherjee, S.; Warshel, A. An effective Coarse-grained model for biological simulations: Recent refinements and validations. Proteins: Struct. Funct. Bioinform. 2014, 82, 1168–1185.
  63. Bai, C.; Wang, J.; Chen, G.; Zhang, H.; An, K.; Xu, P.; Du, Y.; Ye, R.D.; Saha, A.; Zhang, A.; et al. Predicting Mutational Effects on Receptor Binding of the Spike Protein of SARS-CoV-2 Variants. J. Am. Chem. Soc. 2021, 143, 17646–17654.
  64. Bai, C.; Wang, J.; Mondal, D.; Du, Y.; Ye, R.D.; Warshel, A. Exploring the Activation Process of the β2AR-Gs Complex. J. Am. Chem. Soc. 2021, 143, 11044–11051.
  65. Mukherjee, S.; Warshel, A. Electrostatic origin of the mechanochemical rotary mechanism and the catalytic dwell of F1-ATPase. Proc. Natl. Acad. Sci. USA 2011, 108, 20550–20555.
  66. Bai, C.; Warshel, A. Revisiting the protomotive vectorial motion of F0-ATPase. Proc. Natl. Acad. Sci. USA 2019, 116, 19484–19489.
  67. Zhang, Z.; Thirumalai, D. Dissecting the kinematics of the kinesin step. Structure 2012, 20, 628–640.
  68. Warshel, A.; Sharma, P.K.; Kato, M.; Parson, W.W. Modeling electrostatic effects in proteins. Biochim. Biophys. Acta Proteins Proteom. 2006, 1764, 1647–1676.
  69. Marrone, T.J.; Briggs, A.J.M.; McCammon, J.A. Structure-based drug design: Computational advances. Annu. Rev. Pharmacol. Toxicol. 1997, 37, 71–90.
  70. Raha, K.; Peters, M.B.; Wang, B.; Yu, N.; Wollacott, A.M.; Westerhoff, L.M.; Merz, K.M. The role of quantum mechanics in structure-based drug design. Drug Discov. Today 2007, 12, 725–731.
  71. Enyedy, I.J.; Egan, W.J. Can we use docking and scoring for hit-to-lead optimization? J. Comput. Aided Mol. Des. 2008, 22, 161–168.
  72. Guterres, H.; Im, W. Improving protein-ligand docking results with high-throughput molecular dynamics simulations. J. Chem. Inf. Model. 2020, 60, 2189–2198.
  73. Berishvili, V.; Kuimov, A.; Voronkov, A.; Radchenko, E.; Kumar, P.; Choonara, Y.; Pillay, V.; Kamal, A.; Palyulin, V. Discovery of novel tankyrase inhibitors through molecular docking-based virtual screening and molecular dynamics simulation studies. Molecules 2020, 25, 3171.
  74. Halgren, T.A.; Damm, W. Polarizable force fields. Curr. Opin. Struct. Biol. 2001, 11, 236–242.
  75. Arodola, O.A.; Soliman, M.E. Quantum mechanics implementation in drug-design workflows: Does it really help? Drug Des. Dev. Ther. 2017, 11, 2551.
  76. Peters, M.B.; Raha, K.; Merz, K. Quantum mechanics in structure-based drug design. Curr. Opin. Drug Discov. Dev. 2006, 9, 370.
  77. Ribeiro, A.J.; Santos-Martins, D.; Russo, N.; Ramos, M.J.; Fernandes, P.A. Enzymatic flexibility and reaction rate: A QM/MM study of HIV-1 protease. ACS Catal. 2015, 5, 5617–5626.
  78. Uddin, N.; Ahmed, S.; Khan, A.; Hoque, M.M.; Halim, M.A. Halogenated derivatives of methotrexate as human dihydrofolate reductase inhibitors in cancer chemotherapy. J. Biomol. Struct. Dyn. 2019, 38, 901–917.
  79. Nakliang, P.; Lazim, R.; Chang, H.; Choi, S. Multiscale molecular modeling in G protein-coupled receptor (GPCR)-ligand studies. Biomolecules 2020, 10, 631.
  80. Zhou, Y.; Wang, S.; Zhang, Y. Catalytic reaction mechanism of acetylcholinesterase determined by Born-Oppenheimer ab initio QM/MM molecular dynamics simulations. J. Phys. Chem. B 2010, 114, 8817–8825.
  81. Chen, X.; Fang, L.; Liu, J.; Zhan, C.-G. Reaction pathway and free energy profile for butyrylcholinesterase-catalyzed hydrolysis of acetylcholine. J. Phys. Chem. B 2011, 115, 1315–1322.
  82. Cheng, Y.; Cheng, X.; Radić, Z.; McCammon, J.A. Acetylcholinesterase: Mechanisms of covalent inhibition of wild-type and H447I mutant determined by computational analyses. J. Am. Chem. Soc. 2007, 129, 6562–6570.
  83. Liu, J.; Zhang, Y.; Zhan, C.-G. Reaction pathway and free-energy barrier for reactivation of dimethylphosphoryl-inhibited human acetylcholinesterase. J. Phys. Chem. B 2009, 113, 16226–16236.
  84. Olivieri, L.; Gardebien, F. Structure-affinity properties of a high-affinity ligand of FKBP12 studied by molecular simulations of a binding intermediate. PLoS ONE 2014, 9, e114610.
  85. Tosso, R.D.; Andujar, S.A.; Gutierrez, L.; Angelina, E.; Rodriguez, R.; Nogueras, M.; Baldoni, H.; Suvire, F.D.; Cobo, J.; Enriz, R.D. Molecular modeling study of dihydrofolate reductase inhibitors. Molecular dynamics simulations, quantum mechanical calculations, and experimental corroboration. J. Chem. Inf. Model. 2013, 53, 2018–2032.
  86. Cho, A.E.; Guallar, V.; Berne, B.J.; Friesner, R. Importance of accurate charges in molecular docking: Quantum mechanical/molecular mechanical (QM/MM) approach. J. Comput. Chem. 2005, 26, 915–931.
  87. Raha, K.; Merz, K.M. Large-scale validation of a quantum mechanics based scoring function: Predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes. J. Med. Chem. 2005, 48, 4558–4575.
  88. Gleeson, M.P.; Gleeson, D. QM/MM calculations in drug discovery: A useful method for studying binding phenomena? J. Chem. Inf. Model. 2009, 49, 670–677.
  89. Zhang, X.; Zhao, Y.; Lu, G. Recent development in quantum mechanics/molecular mechanics modeling for materials. Int. J. Multiscale Comput. Eng. 2012, 10, 65–82.
  90. Cavasotto, C.N.; Adler, N.S.; Aucar, M.G. Quantum chemical approaches in structure-based virtual screening and lead optimization. Front. Chem. 2018, 6, 188.
  91. Fong, P.; McNamara, J.P.; Hillier, I.H.; Bryce, R.A. Assessment of QM/MM scoring functions for molecular docking to HIV-1 protease. J. Chem. Inf. Model. 2009, 49, 913–924.
  92. Kim, M.; Cho, A.E. Incorporating QM and solvation into docking for applications to GPCR targets. Phys. Chem. Chem. Phys. 2016, 18, 28281–28289.
  93. Chaskar, P.; Zoete, V.; Röhrig, U.F. On-the-Fly QM/MM Docking with Attracting Cavities. J. Chem. Inf. Model. 2017, 57, 73–84.
  94. Whitfield, J.D.; Love, P.J.; Aspuru-Guzik, A. Computational complexity in electronic structure. Phys. Chem. Chem. Phys. 2013, 15, 397–411.
  95. Orús, R.; Latorre, J.I. Universality of entanglement and quantum-computation complexity. Phys. Rev. A 2004, 69, 052308.
  96. Senn, H.M.; Thiel, W. QM/MM methods for biomolecular systems. Angew. Chem. Int. Ed. 2009, 48, 1198–1229.
  97. Wilson, E.; Vant, J.; Layton, J.; Boyd, R.; Lee, H.; Turilli, M.; Hernández, B.; Wilkinson, S.; Jha, S.; Gupta, C. Large-Scale Molecular Dynamics Simulations of Cellular Compartments. In Structure and Function of Membrane Proteins; Springer: Berlin/Heidelberg, Germany, 2021; pp. 335–356.
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , , , , ,
View Times: 266
Revisions: 3 times (View History)
Update Date: 28 Nov 2022