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ContentsPlatform Notes and Installation
Usage
FeaturesAccuracy AutoDock Vina significantly improves the average accuracy of the binding mode predictions compared to AutoDock 4, judging by our tests on the training set used in AutoDock 4 development.[*]
Additionally and independently, AutoDock Vina has been tested against a virtual screening benchmark called the Directory of Useful Decoys by the Watowich group, and was found to be'a strong competitor against the other programs, and at the top of the pack in many cases'. It should be noted that all six of the otherdocking programs, to which it was compared, are distributed commercially. 
AutoDock Tools Compatibility
 For its input and output, Vina uses the same PDBQT molecular structure file format used by AutoDock. PDBQT files can be generated (interactively or in batch mode) and viewed using MGLTools. Other files, such as the AutoDock and AutoGrid parameter files (GPF, DPF) and grid map files are not needed.

Binding mode prediction accuracy on the test set. 'AutoDock' refers to AutoDock 4, and 'Vina' to AutoDock Vina 1.
Ease of Use
 Vina's design philosophy is not to require the user to understand its implementation details, tweak obscure search parameters, cluster results or know advanced algebra (quaternions). All that is required is the structures of the molecules being docked and the specification of the search space including the binding site. Calculating grid maps and assigning atom charges is not needed. The usage summary can be printed with 'vina --help'. The summary automatically remains in sync with the possible usage scenarios. 
Implementation Quality
By design, the results should not have a statistical bias related to the conformation of the input structure. 
Attention is paid to checking the syntactic correctness of the input and reporting errors to the user in a lucid manner. 
The invariance of the covalent bond lengths is automatically verified in the output structures. 
Vina avoids imposing artificial restrictions, such as the number of atoms in the input, the number of torsions, the size of the search space, the exhaustiveness of the search, etc. 
Flexible Side Chains
 Like in AutoDock 4, some receptor side chains can be chosen to be treated as flexible during docking.
SpeedAutoDock Vina tends to be faster than AutoDock 4 by orders of magnitude.[*]
Multiple CPUs/Cores
Additionally, Vina can take advantage of multiple CPUs or CPU cores on your system to significantly shorten its running time.
World Community Grid
Qualified projects can run AutoDock Vina calculations for free on the massively parallel World Community Grid.Existing projects using AutoDock Vina there include those targetingAIDS,Malaria,Leishmaniasis andSchistosomiasis.Some of these projects average over 50 years worth of computation per day.

Average time per receptor-ligand pair on the test set.'AutoDock' refers to AutoDock 4, and 'Vina' to AutoDock Vina 1.
LicenseAutoDock Vina is released under a very permissive Apache license, with few restrictions on commercial or non-commercial use, or on the derivative works.The text of the license can be found here.TutorialIf you have never used AutoDock Vina before, please study the Video Tutorial before attempting to use it. Frequently Asked QuestionsHow to get started learning to use Vina?
Watching the video tutorial might be the best way to do that.
What is the meaning or significance of the name 'Vina'? Why was it developed?
Please see this mailing list post.
How accurate is AutoDock Vina?
See Features
Bowser jrs basics in toys and school mac os. It should be noted that the predictive accuracy varies a lot depending on the target, so it makes sense to evaluate AutoDock Vina against your particular target first,if you have known actives, or a bound native ligand structure, before ordering compounds. While evaluating any docking engine in a retrospective virtual screen, it might make sense to select decoys of similar size, and perhaps other physical characteristics,to your known actives.
What is the difference between AutoDock Vina and AutoDock 4?
AutoDock 4 (and previous versions) and AutoDock Vina were both developed in the Molecular Graphics Lab atThe Scripps Research Institute. AutoDock Vina inherits some of the ideas and approaches of AutoDock 4, such as treating docking as a stochastic global opimization of the scoring function, precalculating grid maps (Vina does that internally), and some other implementation tricks, such asprecalculating the interaction between every atom type pair at every distance. It also uses the same type of structure format (PDBQT) for maximum compatibility with auxiliary software.
However, the source code, the scoring funcion and the actual algorithms used are brand new,so it's more correct to think of AutoDock Vina as a new 'generation' rather than 'version' of AutoDock. The performance was compared in the original publication [*], and on average, AutoDock Vina didconsiderably better, both in speed and accuracy. However, for any given target, either program may provide a better result, even though AutoDock Vina is more likely to do so.This is due to the fact that the scoring functions are different, and both are inexact.
What is the difference between AutoDock Vina and AutoDock Tools?
AutoDock Tools is a module within the MGL Tools software package specifically for generating input (PDBQT files) forAutoDock or Vina. It can also be used for viewing the results.
Can I dock two proteins with AutoDock Vina?
You might be able to do that, but AutoDock Vina is designed only for receptor-ligand docking. There are better programs for protein-protein docking.
Will Vina run on my 64-bit machine?
Yes. By design, modern 64-bit machines can run 32-bit binaries natively.
Why do I get 'can not open conf.txt' error? The file exists!
Oftentimes, file browsers hide the file extension, so while you think you have a file 'conf.txt', it's actually called 'conf.txt.txt'.This setting can be changed in the control panel or system preferences.
You should also make sure that the file path you are providing is correct with respect to the directory (folder) you are in, e.g. if you are referring simply to conf.txt in the command line, make sure you are in the same directory (folder)as this file. You can use ls or dir commands on Linux/MacOS and Windows, respectively, to list the contentsof your directory.
Why do I get 'usage errors' when I try to follow the video tutorial?
The command line options changed somewhat since the tutorial has been recorded. In particular, '--out' replaced '--all'.
Vina runs well on my machine, but when I run it on my exotic Linux cluster, I get a 'boost thread resource' error. Why?
Your Linux cluster is [inadvertantly] configured in such a way as to disallow spawning threads. Therefore, Vina can not run. Contact your system administrator.
Why is my docked conformation different from what you get in the video tutorial?
The docking algorithm is non-deterministic. Even though with this receptor-ligand pair, the minimum of the scoring function corresponds to the correct conformation,the docking algorithm sometimes fails to find it. Try several times and see for yourself. Note that the probability of failing to find the mininum may be different with a different system.
My docked conformation is the same, but my energies are different from what you get in the video tutorial. Why?
The scoring function has changed since the tutorial was recorded, but only in the part that is independent of the conformation:the ligand-specific penalty for flexibility has changed.
Why do my results look weird in PyMOL?
PDBQT is not a standard molecular structure format. The version of PyMOL used in the tutorial (0.99rc6) happens to display it well (because PDBQT is somewhat similar to PDB).This might not be the case for newer versions of PyMOL.
Any other way to view the results?
You can also view PDBQT files in PMV (part of MGL Tools), or convert them into a different file format (e.g. using AutoDock Tools, or with 'save as' in PMV)
How big should the search space be?
As small as possible, but not smaller. The smaller the search space, the easier it is for the docking algorithm to explore it.On the other hand, it will not explore ligand and flexible side chain atom positions outside the search space. You should probably avoid search spaces bigger than 30 x 30 x 30 Angstrom, unless you also increase '--exhaustiveness'.
Why am I seeing a warning about the search space volume being over 27000 Angstrom^3?
This is probably because you intended to specify the search space sizes in 'grid points' (0.375 Angstrom), as in AutoDock 4.The AutoDock Vina search space sizes are given in Angstroms instead. If you really intended to use an unusuallylarge search space, you can ignore this warning, but note that the search algorithm's job may be harder.You may need to increase the value of the exhaustiveness to make up for it. This will lead to longer run time.
The bound conformation looks reasonable, except for the hydrogens. Why?
AutoDock Vina actually uses a united-atom scoring function, i.e. one that involves only the heavy atoms.Therefore, the positions of the hydrogens in the output are arbitrary.The hydrogens in the input file are used to decide which atoms can be hydrogen bond donors or acceptors though,so the correct protonation of the input structures is still important.
What does 'exhaustiveness' really control, under the hood?
In the current implementation, the docking calculation consists of a number of independent runs, starting from random conformations.Each of these runs consists of a number of sequential steps. Each step involves a random perturbation of the conformation followedby a local optimization (using the Broyden-Fletcher-Goldfarb-Shanno algorithm) and a selection in which the step is either accepted or not. Each local optimization involves many evaluations of the scoring function as well asits derivatives in the position-orientation-torsions coordinates.The number of evaluations in a local optimization is guided by convergence and other criteria.The number of steps in a run is determined heuristically, depending on the size and flexibility of the ligand and the flexible side chains. However, the number of runs is set by the exhaustiveness parameter. Since the individual runs are executed in parallel, where appropriate, exhaustiveness also limits the parallelism.Unlike in AutoDock 4, in AutoDock Vina, each run can produce several results: promising intermediate results are remembered.These are merged, refined, clustered and sorted automatically to produce the final result.
Why do I not get the correct bound conformation?
It can be any of a number of things:
If you are coming from AutoDock 4, a very common mistake is to specify the search space in 'points' (0.375 Angstrom), instead of Angstroms. 
Your ligand or receptor might not have been correctly protonated. 
Bad luck (the search algorithm could have found the correct conformation with good probability, but was simply unlucky). Try again with a different seed. 
The minimum of the scoring function correponds to the correct conformation, but the search algorithm has trouble finding it. In this case, higher exhaustiveness or smaller search space should help. 
The minimum of the scoring function simply is not where the correct conformation is. Trying over and over again will not help, but may occasionally give the right answer if two wrongs (inexact search and scoring) make a right. Docking is an approximate approach. 
Related to the above, the culprit may also be the quality of the X-ray or NMR receptor structure. 
If you are not doing redocking, i.e. using the correct induced fit shape of the receptor, perhaps the induced fit effects are large enough to affect the outcome of the docking experiment. 
The rings can only be rigid during docking. Perhaps they have the wrong conformation, affecting the outcome. 
You are using a 2D (flat) ligand as input. 
The actual bound conformation of the ligand may occasionally be different from what the X-ray or NMR structure shows. 
Other problems
How can I tweak the scoring function?
You can change the weights easily, by specifying them in the configuration file,or in the command line. For example
doubles the strenth of all hydrogen bonds.Functionality that would allow the users to create new atom and pseudo-atom types,and specify their own interaction functions is planned for the future.
This should make it easier to adapt the scoring function to specific targets,model covalent docking and macro-cycle flexibility,experiment with new scoring functions,and, using pseudo-atoms, create directional interaction models.
Stay tuned to the AutoDock mailing list, if you wish to be notified of any beta-test releases.
Why don't I get as many binding modes as I specify with '--num_modes'?
This option specifies the maximum number of binding modes to output. The docking algorithm may find fewer 'interesting' binding modes internally.The number of binding modes in the output is also limited by the 'energy_range', which you may want to increase.
Why don't the results change when I change the partial charges?
AutoDock Vina ignores the user-supplied partial charges. It has its own way of dealing with the electrostatic interactions through the hydrophobic andthe hydrogen bonding terms. See the original publication [*] for details of the scoring function.
I changed something, and now the docking results are different. Why?
Firstly, had you not changed anything, some results could have been different anyway, due to the non-deterministic nature of the search algorithm.Exact reproducibility can be assured by supplying the same random seed to both calculations, but only if all other inputs and parameters are the same as well. Even minor changes to the input can have an effect similar to a new random seed.What does make sense discussing arethe statistical properties of the calculations:e.g. 'with the new protonation state, Vina is much less likely to find the correct docked conformation'.
How do I use flexible side chains?
You split the receptor into two parts: rigid and flexible, with the latter represented somewhat similarly to how the ligand is represented. See the section 'Flexible Receptor PDBQT Files' of the AutoDock4.2 User Guide (page 14) for how to do thisin AutoDock Tools.Then, you can issue this command: vina --config conf --receptor rigid.pdbqt --flex side_chains.pdbqt --ligand ligand.pdbqt.Also see this write-up on this subject.
How do I do virtual screening?
Please see the relevant section of the manual. 
Please note that a variety of docking management applications exist to assist you in this task.
I don't have sufficient computing resources to run a virtual screen. What are my options?
You may be able to run your project on the World Community Grid, or use DrugDiscovery@TACC. See Other Software.
I have ideas for new features and other suggestions.
For proposed new features,we like there to be a wide consensus,resulting from a public discussion,regarding their necessity.Please consider starting or joining a discussion on the AutoDock mailing list.
Will you answer my questions about Vina if I email or call you?
No. Vina is community-supported. There is no obligation on the authors to help others with their projects.Please see this page for how to get help.
Platform Notes and InstallationWindowsCompatibilityVina is expected to work on Windows XP and newer systems.InstallingDouble-click the downloaded MSI file and follow the instructionsRunningOpen the Command Prompt and, if you installed Vina in the default location, typeIf you are using Cygwin, the above command would instead beSee the Video Tutorial for details.Don't forget to check out Other Software for GUIs, etc.LinuxCompatibilityVina is expected to work on x86 and compatible 64-bit Linux systems.InstallingOptionally, you can copy the binary files where you want.RunningIf the executable is in your PATH, you can just type '

vina --help' instead.See the Video Tutorial for details.Don't forget to check out Other Software for GUIs, etc.MacCompatibilityThe 64 bit version is expected to work on Mac OS X 10.15 (Catalina) and newer.The 32 bit version of Vina is expected to work on Mac OS X from 10.4 (Tiger) through 10.14 (Mojave).InstallingOptionally, you can copy the binary files where you want.RunningIf the executable is in your PATH, you can just type 'vina --help' instead.See the Video Tutorial for details.Don't forget to check out Other Software for GUIs, etc.Building from SourceAttention: Building Vina from source is NOT meant to be done by regular users!(these instructions might be outdated)Step 1: Install a C++ compiler suiteOn Windows, you may want to install Visual Studio; on OS X, Xcode; and on Linux, the GCC compiler suite.Step 2: Install BoostInstall Boost.(Version 1.41.0 was used to compile the official binaries. With other versions, your luck may vary)Then, build and run one of the example programs, such as the Regex example, to confirm that you have completed this step. If you can't do this, please seek help from the Boost community.Step 3: Build VinaIf you are using Visual Studio, you may want to create three projects: lib, main and split, with the source code from the appropriate subdirectories. lib must be a library, that the other projects depend on, and main and split must beconsole applications. For optimal performance, remember to compile using the Release mode.Artificial Immunity Mac Os DownloadOn OS X and Linux, you may want to navigate to the appropriate build subdirectory, customize the Makefileby setting the paths and the Boost version, and then type
Other SoftwareDisclaimer: This list is for information purposes only and does not constitute an endorsement.Tools specifically designed for use with AutoDock Vina (in no particular order): MGLTools, which includes AutoDock Tools (ADT) and Python Molecular Viewer (PMV). ADT is required for generating input files for AutoDock Vina, and PMV can be used for viewing the results 
PyRx can be used to set up docking and virtual screening with AutoDock Vina and to view the results 
The new Autodock/Vina plugin for PyMOL can be used to set up docking and virtual screening with AutoDock Vina and to view the results 
Computer-Aided Drug-Design Platform using PyMOL is another plugin for PyMOL that also integrates AMBER, Reduce and SLIDE. 
AutoGrow uses AutoDock Vina in its rational drug design procedure 
NNScore will re-score Vina results using an artificial neural network trained on Binding MOAD and PDBBind 
A Vina GUI layer for Windows by Biochem Lab Solutions can be used to facilitate virtual screening with AutoDock Vina 
VSDK can be used to faciliate virtual screening with AutoDock Vina 
PaDEL-ADV can be used to facilitate virtual screening with AutoDock Vina 
DrugDiscovery@TACC allows you to do virtual screening with AutoDock Vina through their web site 
NBCR CADD Pipeline provides access to virtual screening with AutoDock Vina on NBCR computers 
MOLA is a bootable, self-configuring system for virtual screening using AutoDock4/Vina on computer clusters 
SMINA is a modification of Vina that links with OpenBabel for I/O and supports additional tweaks of the scoring function 
Off-Target Pipeline is a platform intended to carry out secondary target identification and docking 
AUDocker can be used to facilitate virtual screening with AutoDock Vina 
World Community Grid can be used by qualified projects to run AutoDock Vina calculations for free on the massively parallel network of computers, where volunteers donate their idle CPU time. 
DockoMatic is a graphical user interface intended to facilitate virtual screening with AutoDock and AutoDock Vina. 
VinaLC is a modification of Vina by the Lawrence Livermore National Laboratory that takes advantage of MPI on computer clusters 
Other tools that you are likely to find useful while docking or virtual screening with AutoDock Vina: PyMOL is one of the most popular programs for molecular visualization and can be used for viewing the docking results 
OpenBabel can be used to convert among various structure file formats, assign the protonation states, etc. 
ChemAxon Marvin can be used to visualize structures, convert among various structure file formats, assign the protonation states, etc. 
UsageSummaryThe usage summary can be obtained with 'vina --help':Configuration fileFor convenience, some command line options can be placed into a configuration file.For example:
In case of a conflict, the command line option takes precedence over the configuration file one.Search spaceThe search space effectively restricts where the movable atoms, including those in the flexible side chains, should lie.ExhaustivenessWith the default (or any given) setting of exhaustiveness, the time spent on the search is already varied heuristically depending on the number of atoms, flexibility, etc. Normally, it does not make sense to spend extra time searching to reduce the probability of not finding the global minimum of the scoring function beyond what is significantly lower than the probability that the minimum is far from the native conformation.However, if you feel that the automatic trade-off made between exhaustiveness and time is inadequate, you can increase the exhaustiveness level. This should increase the time linearly and decrease the probability of not finding the minimum exponentially.OutputArtificial Immunity Mac Os XEnergyThe predicted binding affinity is in Artificial Immunity Mac Os 11kcal/mol.RMSDRMSD values are calculated relative to the best mode and use only movable heavy atoms. Two variants of RMSD metrics are provided, rmsd/lb (RMSD lower bound) and rmsd/ub (RMSD upper bound), differing in how the atoms are matched in the distance calculation:rmsd/ub matches each atom in one conformation with itself in the other conformation, ignoring any symmetry 
rmsd' matches each atom in one conformation with the closest atom of the same element type in the other conformation (rmsd' can not be used directly, because it is not symmetric) 
rmsd/lb is defined as follows:rmsd/lb(c1, c2) = max(rmsd'(c1, c2), rmsd'(c2, c1))
Hydrogen positionsVina uses a united-atom scoring function. As in AutoDock, polar hydrogens are needed in the input structures to correctly type heavy atoms as hydrogen bond donors. However, in Vina, the degrees of freedom that only move hydrogens, such as the hydroxyl group torsions, are degenerate. Therefore, in the output, some hydrogen atoms can be expected to be positioned randomly (but consistent with the covalent structure). For a united-atom treatment, this is essentially a cosmetic issue. Separate modelsAll predicted binding modes, including the positions of the flexible side chains are placed into one multimodel PDBQT filespecified by the 'out' parameter or chosen by default, based on the ligand file name. If needed, this file can be splitinto individual models using a separate program called 'vina_split', included in the distribution.Advanced OptionsAutoDock Vina's 'advanced options' are intended to be primarily used by people interested in methods development rather thanthe end users. The usage summary including the advanced options can be shown withThe advanced options allow
scoring without minimization 
performing local optimization only 
randomizing the input with no search (this is useful for testing docking software) 
changing the weights from their default values (see the paper[*] for what the weights mean) 
displaying the individual contributions to the intermolecular score, before weighting (these are shown with '--score_only'; see the paper[2] for what the terms are)
Virtual ScreeningYou may want to choose some of the tools listed under Other Software to perform virtual screening. Alternatively, if you are familiar with shell scripting, you can do virtual screening without them.The examples below assume that Bash is your shell. They will need to be adapted to your specific needs.
Windows
To perform virtual screening on Windows, you can either use Cygwin and the Bash scripts below, or, alternatively, adapt them for the Windows scripting language.
Linux, Mac
Suppose you are in a directory containing your receptor receptor.pdbqt and a set of ligands named ligand_01.pdbqt, ligand_02.pdbqt, etc.
You can create a configuration file conf.txt, such as
and dock all ligands with this shell script.The script assumes that vina is in your PATH.Otherwise, modify it accordingly.PBS Cluster
If you have a Linux Beowulf cluster,you can perform the individual dockings in parallel. 
Continuing with our example, instead of executing all the dockings in a loop locally,we will write one *.job script per ligand,and use qsub (a PBS command)to schedule these scripts to be executed by the cluster.
Run this shell script to do it.The script assumes that vina and qsub are in your PATH.Otherwise, modify it accordingly.
Once the jobs have been scheduled, you can monitor their status with
Selecting Best Results
If you are on Unix and in a directory that contains directories with PDBQT files, all of which are AutoDock Vina results,you may find this Python script useful for selecting the top results. Run it as:
to get the file names of the top 10 hits, which can then be easily copied. HistoryBrief summaries of changes between versions can be foundhere.CitationIf you used AutoDock Vina in your work, please cite:O. Trott, A. J. Olson,AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading,Journal of Computational Chemistry 31 (2010) 455-461Getting HelpPlease seethis pageif you have questions about AutoDock Vina.Reporting BugsPotential bug reports are greatly appreciated, even if you are not exactly sure that they are bugs.However, please do not include requests for assistance along with your bug report.See this pageinsead.Likely bugs:Early termination 
Failure to terminate 
Changes of the covalent lengths or of the invariant angles in the output 
'Obviously wrong' clashes (check your 'search space' though) 
Disagreement with the documentation
Likely not bugs:Anything that happens before you run Vina or after it finished 
Occasional disagreement with the experiment 
Vina's refusal to open a file that does not exist (e.g. try ls conf.txt to see if the file is really there)
ReportingYou can send your reports to the AutoDock mailing list. Please remember to provide a descriptive 'Subject' line and all of the information needed to reproduce the problem you are seeing.In artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modeled after the immune system's characteristics of learning and memory for use in problem-solving.
Definition[edit]Artificial Immunity Mac Os CatalinaThe field of Artificial Immune Systems (AIS) is concerned with abstracting the structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. AIS is a sub-field of Biologically-inspired computing, and Natural computation, with interests in Machine Learning and belonging to the broader field of Artificial Intelligence.
Artificial Immune Systems (AIS) are adaptive systems, inspired by theoretical immunology and observed immune functions, principles and models, which are applied to problem solving.[1] Bloody mary mac os.
AIS is distinct from computational immunology and theoretical biology that are concerned with simulating immunology using computational and mathematical models towards better understanding the immune system, although such models initiated the field of AIS and continue to provide a fertile ground for inspiration. Finally, the field of AIS is not concerned with the investigation of the immune system as a substrate for computation, unlike other fields such as DNA computing.
History[edit]AIS emerged in the mid-1980s with articles authored by Farmer, Packard and Perelson (1986) and Bersini and Varela (1990) on immune networks. However, it was only in the mid-1990s that AIS became a field in its own right. Forrest et al. (on negative selection) and Kephart et al.[2] published their first papers on AIS in 1994, and Dasgupta conducted extensive studies on Negative Selection Algorithms. Hunt and Cooke started the works on Immune Network models in 1995; Timmis and Neal continued this work and made some improvements. De Castro & Von Zuben's and Nicosia & Cutello's work (on clonal selection) became notable in 2002. The first book on Artificial Immune Systems was edited by Dasgupta in 1999.
Currently, new ideas along AIS lines, such as danger theory and algorithms inspired by the innate immune system, are also being explored. Although some believe that these new ideas do not yet offer any truly 'new' abstract, over and above existing AIS algorithms. This, however, is hotly debated, and the debate provides one of the main driving forces for AIS development at the moment. Other recent developments involve the exploration of degeneracy in AIS models,[3][4] which is motivated by its hypothesized role in open ended learning and evolution.[5][6]
Originally AIS set out to find efficient abstractions of processes found in the immune system but, more recently, it is becoming interested in modelling the biological processes and in applying immune algorithms to bioinformatics problems.
In 2008, Dasgupta and Nino [7] published a textbook on Immunological Computation which presents a compendium of up-to-date work related to immunity-based techniques and describes a wide variety of applications.
Techniques[edit]The common techniques are inspired by specific immunological theories that explain the function and behavior of the mammalianadaptive immune system.
Clonal Selection Algorithm: A class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen–antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation. Clonal selection algorithms are most commonly applied to optimization and pattern recognition domains, some of which resemble parallel hill climbing and the genetic algorithm without the recombination operator.[8]
Negative Selection Algorithm: Inspired by the positive and negative selection processes that occur during the maturation of T cells in the thymus called T cell tolerance. Negative selection refers to the identification and deletion (apoptosis) of self-reacting cells, that is T cells that may select for and attack self tissues. This class of algorithms are typically used for classification and pattern recognition problem domains where the problem space is modeled in the complement of available knowledge. For example, in the case of an anomaly detection domain the algorithm prepares a set of exemplar pattern detectors trained on normal (non-anomalous) patterns that model and detect unseen or anomalous patterns.[9]
Immune Network Algorithms: Algorithms inspired by the idiotypic network theory proposed by Niels Kaj Jerne that describes the regulation of the immune system by anti-idiotypic antibodies (antibodies that select for other antibodies). This class of algorithms focus on the network graph structures involved where antibodies (or antibody producing cells) represent the nodes and the training algorithm involves growing or pruning edges between the nodes based on affinity (similarity in the problems representation space). Immune network algorithms have been used in clustering, data visualization, control, and optimization domains, and share properties with artificial neural networks.[10]
Dendritic Cell Algorithms: The Dendritic Cell Algorithm (DCA) is an example of an immune inspired algorithm developed using a multi-scale approach. This algorithm is based on an abstract model of dendritic cells (DCs). The DCA is abstracted and implemented through a process of examining and modeling various aspects of DC function, from the molecular networks present within the cell to the behaviour exhibited by a population of cells as a whole. Within the DCA information is granulated at different layers, achieved through multi-scale processing.[11]
See also[edit]Notes[edit]^de Castro, Leandro N.; Timmis, Jonathan (2002). Artificial Immune Systems: A New Computational Intelligence Approach. Springer. pp. 57–58. ISBN978-1-85233-594-6.
^Kephart, J. O. (1994). 'A biologically inspired immune system for computers'. Proceedings of Artificial Life IV: The Fourth International Workshop on the Synthesis and Simulation of Living Systems. MIT Press. pp. 130–139.
^Andrews and Timmis (2006). A Computational Model of Degeneracy in a Lymph Node. Lecture Notes in Computer Science. 4163. pp. 164–177. doi:10.1007/11823940_13. ISBN978-3-540-37749-8. S2CID2539900.
^Mendao; et al. (2007). 'The Immune System in Pieces: Computational Lessons from Degeneracy in the Immune System'. Foundations of Computational Intelligence (FOCI): 394–400. doi:10.1109/FOCI.2007.371502. ISBN978-1-4244-0703-3. S2CID5370645.
^Edelman and Gally (2001). 'Degeneracy and complexity in biological systems'. Proceedings of the National Academy of Sciences of the United States of America. 98 (24): 13763–13768. Bibcode:2001PNAS..9813763E. doi:10.1073/pnas.231499798. PMC61115. PMID11698650.
^Whitacre (2010). 'Degeneracy: a link between evolvability, robustness and complexity in biological systems'. Theoretical Biology and Medical Modelling. 7 (6): 6. doi:10.1186/1742-4682-7-6. PMC2830971. PMID20167097.
^Dasgupta, Dipankar; Nino, Fernando (2008). Immunological Computation: Theory and Applications. CRC Press. p. 296. ISBN978-1-4200-6545-9.
^de Castro, L. N.; Von Zuben, F. J. (2002). 'Learning and Optimization Using the Clonal Selection Principle'(PDF). IEEE Transactions on Evolutionary Computation. 6 (3): 239–251. doi:10.1109/tevc.2002.1011539.
^Forrest, S.; Perelson, A.S.; Allen, L.; Cherukuri, R. (1994). 'Self-nonself discrimination in a computer'(PDF). Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy. Los Alamitos, CA. pp. 202–212.
^Timmis, J.; Neal, M.; Hunt, J. (2000). 'An artificial immune system for data analysis'(PDF). BioSystems. 55 (1): 143–150. doi:10.1016/S0303-2647(99)00092-1. PMID10745118.
^Greensmith, J.; Aickelin, U. (2009). Artificial Dendritic Cells: Multi-faceted Perspectives(PDF). Human-Centric Information Processing Through Granular Modelling. Studies in Computational Intelligence. 182. pp. 375–395. CiteSeerX10.1.1.193.1544. doi:10.1007/978-3-540-92916-1_16. ISBN978-3-540-92915-4. Archived from the original(PDF) on 2011-08-09. Retrieved 2009-06-19.
References[edit]J.D. Farmer, N. Packard and A. Perelson, (1986) 'The immune system, adaptation and machine learning', Physica D, vol. 2, pp. 187–204
H. Bersini, F.J. Varela, Hints for adaptive problem solving gleaned from immune networks. Parallel Problem Solving from Nature, First Workshop PPSW 1, Dortmund, FRG, October, 1990.
D. Dasgupta (Editor), Artificial Immune Systems and Their Applications, Springer-Verlag, Inc. Berlin, January 1999, ISBN3-540-64390-7
V. Cutello and G. Nicosia (2002) 'An Immunological Approach to Combinatorial Optimization Problems' Lecture Notes in Computer Science, Springer vol. 2527, pp. 361–370.
L. N. de Castro and F. J. Von Zuben, (1999) 'Artificial Immune Systems: Part I -Basic Theory and Applications', School of Computing and Electrical Engineering, State University of Campinas, Brazil, No. DCA-RT 01/99.
S. Garrett (2005) 'How Do We Evaluate Artificial Immune Systems?' Evolutionary Computation, vol. 13, no. 2, pp. 145–178. http://mitpress.mit.edu/journals/pdf/EVCO_13_2_145_0.pdf
V. Cutello, G. Nicosia, M. Pavone, J. Timmis (2007) An Immune Algorithm for Protein Structure Prediction on Lattice Models, IEEE Transactions on Evolutionary Computation, vol. 11, no. 1, pp. 101–117. https://web.archive.org/web/20120208130715/http://www.dmi.unict.it/nicosia/papers/journals/Nicosia-IEEE-TEVC07.pdf
Villalobos-Arias M., Coello C.A.C., Hernández-Lerma O. (2004) Convergence Analysis of a Multiobjective Artificial Immune System Algorithm. In: Nicosia G., Cutello V., Bentley P.J., Timmis J. (eds) Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. DOI https://doi.org/10.1007/978-3-540-30220-9_19
External links[edit]AISWeb: The Online Home of Artificial Immune Systems Information about AIS in general and links to a variety of resources including ICARIS conference series, code, teaching material and algorithm descriptions.
ARTIST: Network for Artificial Immune Systems Provides information about the UK AIS network, ARTIST. It provides technical and financial support for AIS in the UK and beyond, and aims to promote AIS projects.
Computer Immune Systems Group at the University of New Mexico led by Stephanie Forrest.
AIS: Artificial Immune Systems Group at the University of Memphis led by Dipankar Dasgupta.
IBM Antivirus Research Early work in AIS for computer security.
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