PerspectivesAre you interested in submitting a Perspective Article? Be sure to read The Science Advisory Board's Editorial Guides for Perspective Articles. Click here. Cheminformatics: Rationalizing the Drug Design In Silico by Prashanth Suravajhala, M.Sc. Chem-informatics or chemo-informatics? Computational chemistry, cheminformatics, bio molecular informatics, chemical informatics, and bio-chemo informatics are synonymous. All these pave the way for pioneering chemical informatics. All are tantamount. However, the word cheminformatics sounds great. So this review presets the word cheminformatics and considers it indistinguishable from the various words mentioned above. Cheminformatics & the Life Sciences: Cheminformatics is an inter-disciplinary subject of storage, processing and retrieval of chemical information in silico. It has been the descendant of bioinformatics and has value-guided objectives like extracting, envisaging and elucidating the chemical data, coined as three �E�s. Chemical Information is information relating to chemical structures or their properties. On the other hand, this subject has given enough impetus in the hands of the stream basic life sciences. It is more or synonymously used with computational chemistry that is the application of mathematical and computational methods to chemistry. The whole of cheminformatics is a fusion of many facets of science including math, molecular biology, statistics, and biochemistry in addition to chemistry. However, this field would be inquisitive without molecular modeling where in 3D graphics and optimization techniques are preferentially used to help scientists understand how drugs bind to proteins in the body. The discipline of using computers for the discovery and design of drugs is named as Computer-Aided Drug Design. All the above techniques are used in this so-called field that uses techniques from the fields of chemoinformatics, computational chemistry and molecular modeling. Cheminformatics, by and large has become a decision making tool in drug discovery. Paradigm Shift in Pharmaceutical Research: Today, the extent of informatics has enriched other allied subjects, be it biology, chemistry or any life science stream. Numerous biochemical and structural studies have shown the conformations of various receptors that could be influenced by ligand binding. While we have various web based Cheminformatics and molecular property prediction tools that supporting drug design and development, these tools offer many advantages for processing chemical information with a vivid use. This has provided the way for pharmaceutical companies to use such sophisticated web technologies for delivering molecular processing tools directly to the wet-lab chemists and assist in the process of designing and development of new drugs. The technologies include all wet-lab enabled in silico calculations of molecular properties, property-based virtual screening, and visualization of molecules, bio isosteric design, diversity analysis, and support of combinatorial chemistry. Drug Discovery: There is a choice of tools that are available today that are targeted for drug discovery applications. Apart from the cheminformatics and microarray analysis tools, the age of pharmacogenomics has enabled discovery researchers to use complicated predictive high throughput screening. The compounds are primarily screened with number of assays. Before validating them, the assay activity is done. At present, discovery researchers in many biopharmaceutical companies are using microarray data in which gene expression microarrays are a powerful experimental platform for studies of functional genomics, toxicogenomics, subtyping and a host of other applications. Differential expression analysis is a key analysis component in most studies. A major goal of such analyses is to identify genes that are differentially expressed between experimental conditions, while keeping the probability of false discoveries acceptably low. Given the many sources of variability in microarray measurements, the need for experimental design, replication and statistically rigorous pre-processing and differential expression analysis is now widely recognized. Cheminformatics in Allied Fields: Cheminformatics is a tool that aims at facilitating the decision-making process across various preclinical stages of drug discovery. Access to biological and chemical data, but not the data themselves, is an integral part of cheminformatics. Emerging tools that allow storage of, and access to, chemical, structural-chemical and biological information are only now beginning to reach maturity. Recent advances in cheminformatics include virtual library analysis without enumeration and novel methods to investigate global chemical similarity and diversity voids. The most important task for cheminformatics is to constantly reevaluate itself and its utility in the area of drug discovery, in order to provide probabilistic, rather than categorical predictions. The emergence of ~omics(genomics, proteomics, metabolomics, transcriptomics etc) has threadbare novel approaches in many fields. Expression profiling shall be useful in the diagnoses of various diseases, in preclinical phases of drug development and in developing markers for unhelpful drug reactions and desired pharmacological effects. All the way through, cheminformatics decisions could be made in drug discovery, tailor-made to the individual needs of the patient at the right dosage at right time. With the entire human genome map now completely mapped, gene-based single nucleotide polymorphism will be valuable in the diagnosis of diseases. This has also given an weight to fragment analysis in small molecule discovery, with the widespread use of high-throughput screening (HTS) and combinatorial chemistry techniques which have led to the generation of large amounts of pharmacological data which, in turn, has catalyzed the development of computational methods designed to reduce the time and cost in identifying molecules suitable for pharmaceutical development. The how of a tool for decision-makers in drug discovery is no doubt CHEMINFORMATICS. High-Throughput Screening Traditionally, small numbers of compounds were tested. With the advent of technology, nowadays we can test 100,000 compounds a day for activity against a protein target using HTS. This should help us in tracing a said activity of the protein. All a chemist needs to know is to intelligently select the varied classes of compounds that show the most promise for being drugs to follow-up. Drug companies now have millions of samples of chemical compounds Tools for Mining the Data: Retrieving the enormous amount of data is called as data mining. X-ray crystallography and NMR Spectroscopy can reveal 3 dimensional structures of protein and bound compounds. This should improvise the design of a drug using visualization of active molecules using in silico approaches. The following are the visualization techniques used: Superposing molecules Detecting Pharmacophores Calculating similarity between molecules Visualizing molecular similarity Building a model of activity Pharmacophore model generation Inferring how of molecules that might bind to protein Computer-Aided Combinatorial Chemistry: Selection and validation of novel molecular targets has become a paramount importance in light of the plethora of new potential therapeutic drug targets that have emerged from human genome sequencing. In response to this revolution within the pharmaceutical industry, the development of high-throughput methods in both biology and chemistry has been necessitated. Data Base Implementation: Storing biological data for millions of data points, computational representation of 2D structure, need to be able to organize thousands of active compounds into meaningful groups. The recent advances in laboratory technologies have resulted in a wealth of chemical and biological data. The rapid proliferation of a vast amount of data has led to a set of cheminformatics and bioinformatics applications that manipulate dynamic, heterogeneous, and massive data. An example of such application in the pharmaceutical industry is the computational process involved in the early discovery of lead drug candidates for a given target disease. There are various trends in virtual combinatorial library design like similarity-based compound clustering techniques, structure-based docking and scoring, and fragment-based de-novo design. Areas of Application: Though, the wide use of cheminformatics tools hasn�t reached many drug modules until now, it has high source of application in the following areas: Life sciences: In identifying the disease and isolating a protein, nevertheless is a preliminary objective. The how of finding a drug effective against a disease protein could be done in silico. Pharmaceuticals: Preclinical testing, formulation & scale-up Pro-drug design: Human clinical trials and Food and Drug Administration (FDA) approval Chemical databases: Databases of available chemicals Impact of New Technology on Drug Discovery: The last few years have seen a number of groundbreaking new technologies including transgenics, gene therapy, cloning, stem cell therapy, gene chips, genomics and Human Genome Project (HGP). Of them, HGP is considered as the biggest project where in umpteen number of information has been deciphered which has paved the way for designing the drugs. This information that is accessible today has a revolutionary tone bringing up the novel technologies in the areas of genetic engineering and molecular biology. Molecular tools: There are many Pharmaceutical analysis software used for structure prediction and unlocking biological secrets like EXPASY SWISS MODEL GENO 3D CPH MODELS Now we have programs like Random Forest, a group of increased classification regression trees which works using the bootstrap samples of the data and random feature selection in tree induction. This is used for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. The application of Cheminformatics to High-Throughput Screening (HTS) data requires the use of robust modeling methods. Questionnaire: There are the impending thoughts that usually creep in our minds. It is time we try to accelerate and retrieve the questionnaire. What is the potential to produce vast number of compounds within a short period of time? How can it be best used to screen 100,000 compounds a day for activity against a target protein? How reliable are computer graphics & models for helping and improving activity? Is it worthwhile investing in R&D and new technologies? How are genomics and proteomics technologies going to change this field? How well will the quality of life be improved? Chemists work on these compounds, developing new and more potent compounds. How feasible is it to intergrate this with existing practices. Are all the compounds potent enough for describing the activity of the protein or the drug? Future Innovations: Molecular medicine will shift from costly intervention and treatment of established diseases to proactive prediction and prevention of disease risks. This approach should really require new informatics� systems that will link large scale databanks and special programs for data mining and retrieval in bioinformatics and cheminformatics. All the wet laboratories should be able to provide a platform for powerful new molecular diagnostic tools along with multi analytic assays for expression of genes and proteins in different patterns of diseases, disease progression, and predisposition to diseases. All the in vitro ADME testing would pave way for in silico testing. ### Prashanth Suravajhala has been a member of The Science Advisory Board since April 2003. He is a Ph.D. student in computational molecular biology at Roskilde University in Denmark. Reference: 1: Web-based cheminformatics and molecular property prediction tools supporting drug design and development at Novartis. SAR QSAR Environ Res. 2003 Oct-Dec; 14(5-6):321-8. 2. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003 Nov-Dec; 43(6):1947-58. 3: Cheminformatics analysis of organic substituents: identification of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups. J Chem Inf Comput Sci. 2003 Mar-Apr; 43(2):374-80. 4. In silico prediction of drug-binding strengths to human serum albumin. Med Res Rev. 2003 May; 23(3):275-301. Review. 5. Cheminformatics - decision making in drug discovery. Drug Discov Today. 2002 Sep 1; 7(17):898-900. 6: An efficient implementation of a drug candidate database. J Chem Inf Comput Sci. 2003 Jan-Feb; 43(1):25-35. 7: Trends in virtual combinatorial library design. Curr Med Chem. 2002 Dec;9(23):2095-101. Review. 8: Pharmacogenomics in anticoagulant drug development. Pharmacogenomics. 2002 Nov; 3(6):823-8. Review. 9: An ontology for pharmaceutical ligands and its application for in silico screening and library design. J Chem Inf Comput Sci. 2002 Jul-Aug; 42(4):947-55. 10: Fragment analysis in small molecule discovery. Curr Opin Drug Discov Devel. 2002 May; 5(3):391-9. Review. 11: Cheminformatics: a tool for decision-makers in drug discovery. Curr Opin Drug Discov Devel. 2001 May; 4(3):308-13. Review. 12: Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. 13: PICCOLO: a tool for combinatorial library design via multicriterion optimization. Pac Symp Biocomput. 2000:588-99. 14: Computer-aided combinatorial chemistry and cheminformatics Pac Symp Biocomput. 2000 ;( 12):553-4. 15: Drug discovery in the next millennium. Annu Rev Pharmacol Toxicol. 2000; 40:177-91. Review. 16: Technology that will initiate future revolutionary changes in healthcare and the clinical laboratory. J Clin Lab Anal. 1999; 13(2):49-52. Review. ### << Previous Next >> [ View All Perspectives ] |
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