Computer Automated Structure Evaluation Research Articles

A structural analysis of the differential cytotoxicity of chemicals in the NCI-60 cancer cell lines

The primary functions of cancer chemotherapeutic agents are not only to inhibit the growth or kill the cancer cells, but to do so without eliciting unreasonable cytotoxic effects on the healthy cells and to withstand the ability of the cancer cells to develop resistance against it. This has unfortunately been proven so far to be a very difficult objective. In this perspective, the ability of small molecules (anti- tumor agents) to ‘see’ different cell types differently can be a key attribute. Thus the term ‘differential cytotoxicity’ is normally used to describe the drug’s specificity. In the present paper, we have quantified differential cytotoxicity from a study of the chemicals tested in the National Cancer Institute’s Developmental Therapeutics Program. The MULTICASE (Multiple Computer Automated Structure Evaluation) methodology was used to discover statistically significant structural fragments (biophores) related to the differential cytotoxicity of the compounds. We found that even small structural features often become important in this regard which is evident from the biophores that were found in structurally diverse chemicals. By utilizing the difference between the raw and normalized differential cytotoxicity indices, we found that the α,β-unsaturated carbonyl group (O C C CH 2) is the major biophore associated with compounds with essentially parallel concentration profiles in the cell lines in question. These compounds have high non-normalized differential cytotoxicity but considerably low normalized differential cytotoxocity. The models developed were cross validated for their predictive ability.

Structure–activity considerations and in vitro approaches to assess the genotoxicity of 19 methane-, benzene- and toluenesulfonic acid esters

Sulfonic acid esters are considered as potentially alkylating agents that may exert genotoxic effects in bacterial and mammalian cell systems. One possible source of human exposure stems from drug synthesis when the salt-forming agents methanesulfonic acid, benzenesulfonic acid or p-toluenesulfonic acid are used together with alcoholic solvents such as methanol, ethanol and propanol. In this study computer-assisted structural considerations and in vitro approaches (Ames mutagenicity test using Salmonella typhimurium strains TA98 and TA100, and the micronucleus test using L5178Y mouse lymphoma cells) were used to assess the genotoxic properties of 19 sulfonic esters. While all esters may be principally active as genotoxicants based on the presence of the sulfonate moiety, the statistical correlative multiple computer automated structure evaluation (MCASE) system (MC4PC version 1.0) using the Ames mutagenicity A2I module (version 1.54), rank-ordered the activity of the benzenesulfonic acid esters in the Ames test negligible due to an inactivating modulator and a deactivating fragment, whereas the methane- and toluenesulfonic acid esters were predicted to be positive in this test. In the Ames test, with the exception of the p-toluenesulfonic acid ethyl and iso-butyl esters, all compounds came out positive in Salmonella strain TA100. Methanesulfonic iso-propyl, sec-butyl and benzenesulfonic acid iso-propyl ester also showed mutagenic potential in strain TA98. In general, differences between results seen in Ames tests performed with or without metabolic activation were rather small. In L5178Y mouse lymphoma cells, benzenesulfonic acid n- and iso-butyl ester and p-toluenesulfonic acid iso-butyl ester did not increase the number of cells containing micronuclei. The other esters were positive in this micronucleus test; however, methanesulfonic acid iso-butyl ester was found to be only weakly positive at excessively cytotoxic concentrations. These compounds were generally found to be more potent with regard to micronucleus induction when tested without metabolic activation (20 h treatment). In conclusion, the iso-propyl esters of the three sulfonic acids under study were found to be the strongest mutagens, either when tested in the Ames test or the micronucleus assay, whereas p-toluenesulfonic acid iso-butyl ester was the only compound shown to be devoid of a genotoxic potential in both tests.

Searching for an Enhanced Predictive Tool for Mutagenicity

The Multiple Computer Automated Structure Evaluation (MCASE) program was used to evaluate the mutagenic potential of organic compounds. The experimental Ames test mutagenic activities for 2513 chemicals were collected from various literature sources. All chemicals have experimental results in one or more Salmonella tester strains. A general mutagenicity data set and fifteen individual Salmonella test strain data sets were compiled. Analysis of the learning sets by the MCASE program resulted in the derivation of good correlations between chemical structure and mutagenic activity. Significant improvement was obtained as more data was added to the learning databases when compared with the results of our previous reports. Several biophores were identified as being responsible for the mutagenic activity of the majority of active chemicals in each individual mutagenicity module. It was shown that the multiple-database mutagenicity model showed a clear advantage over normally used single-database models. The expertise produced by this analysis can be used to predict the mutagenic potential of new compounds.

Quantitative structure‐activity relationships for predicting mutagenicity and carcinogenicity

Quantitative structure-activity relationships (QSARs) for predicting mutagenicity and carcinogenicity were reviewed. The QSARs for predicting mutagenicity and carcinogenicity have been mostly limited to specific classes of chemicals (e.g., aromatic amines and heteroaromatic nitro chemicals). The motivation to develop QSARs for predicting mutagenicity and carcinogenicity to screen inventories of chemicals has produced four major commercially available computerized systems that are able to predict these endpoints: Deductive estimation of risk from existing knowledge (DEREK) toxicity prediction by komputer assisted technology (TOPKAT), computer automated structure evaluation (CASE), and multiple computer automated structure evaluation (Multicase). A brief overview of these and some other expert systems for predicting mutagenicity and carcinogenicity is provided. The other expert systems for predicting mutagenicity and carcinogenicity include automatic data analysis using pattern recognition techniques (ADAPT), QSAR Expert System (QSAR-ES), OncoLogic computer optimized molecular parametric analysis of chemical toxicity system (COMPACT), and common reactivity pattern (COREPA).

Multiple computer‐automated structure evaluation study of aquatic toxicity. III. Vibrio fischeri

An acute toxicity model was constructed on the basis of 901 chemicals tested for toxicity against the luminescent bacteria Vibrio fischeri (formerly Photobacterium phosphoreum, the Microtox test). The model was created using the Multiple Computer-Automated Structure Evaluation (M-CASE) program. The model can correctly predict acute toxicity for 92% of the compounds with an error averaging 0.55 log units per median effect concentration (EC50). The main toxicophores, corresponding to polar and nonpolar narcosis, and other types of reactive chemicals were identified.

MCASE study of the multidrug resistance reversal activity of propafenone analogs.

A database containing 130 propafenone type chemicals which have been tested for their multidrug resistance (MDR) reversal activity was compiled. Using the Multiple Computer-Automated Structure Evaluation (MCASE) program to analyze this database, an underlying relationship between MDR reversal activity and octanol/water partition coefficient was found. An MDR reversal model was created based on this database by the baseline activity identification algorithm (BAIA) of the MCASE program. The main phamacophores relevant to MDR reversal activity were identified.

Structure–specificity relationships for haloalkane dehalogenases

A structural analysis of the substrate specificity of hydrolytic dehalogenases originating from three different bacterial isolates has been performed using the multiple computer-automated structure evaluation methodology. This methodology identifies structural fragments in substrate molecules that either activate or deactivate biological processes. The analysis presented in this contribution is based on newly measured dehalogenation data combined with data from the literature (91 substrates). The enzymes under study represent different specificity classes of haloalkane dehalogenases (haloalkane dehalogenase from Xanthobacter autotrophicus GJ10, Rhodococcus erythropolis Y2, and Sphingomonas paucimobilis UT26). Three sets of structural rules have been identified to explain their substrate specificity and to predict activity for untested substrates. Predictions of activity and inactivity based on the structural rules from this analysis were provided for those compounds that were not yet tested experimentally. Predictions were also made for the compounds with available experimental data not used for the model construction (i.e., the external validation set). Correct predictions were obtained for 28 of 30 compounds in the validation set. Incorrect predictions were noted for two substrates outside the chemical domain of the set of compounds for which the structural rules were generated. A mechanistic interpretation of the structural rules generated provided a fundamental understanding of the structure-specificity relationships for the family of haloalkane dehalogenases.

Multiple computer-automated structure evaluation model of the plasma protein binding affinity of diverse drugs

A drug protein binding model was constructed on the basis of protein-affinity data for154 drugs. The Multiple Computer-Automated Structure Evaluation program (M-CASE) was used for the construction of the model, which separates the total data set into groups of drugs with common structural features. For each of these groups, a multiparameter Quantitative Structure–Activity Relationship (QSAR) was obtained. The most general structural fragment for all investigated drugs is a part of the phenyl ring. The lipophilicity represented by the octanol–water partition coefficient was also found to be a significant parameter for each local QSAR. The model was shown to be able to predict correctly the percentage of drug bound in plasma for ∼80% of compounds with an average error of only ∼14%.

Identification of structural features and associated mechanisms of action for carcinogens in rats

A set of chemicals tested for carcinogenicity in rats that have been analyzed in the Carcinogenic Potency Database (CPDB) was subjected to CASE/MULTICASE (a computer-automated structure evaluation system) structure-activity relationship (SAR) analyses. This SAR system identifies structural features of chemicals in a learning set that are associated with a predefined activity and produces an SAR model based on these characteristics. The rat CPDB used in this study consisted of 745 chemicals, 383 of which are carcinogens, 14 marginally active carcinogens (i.e., chemicals that require a relatively high dose to induce carcinogenesis) and 348 are non-carcinogens. In an internal prediction analysis where CASE/MULTICASE ‘predicted’ the activity of chemicals in the learning set, the system was able to achieve a concordance between experimental and predicted results of 95%. This indicates that the program is able to adequately assess the chemicals in the database. In a 10-fold cross-validation study where 10 disjoint sets of 10% of the chemicals were removed from the database and the remaining 90% of the chemicals were used as a learning set, CASE/MULTICASE was able to achieve a concordance between experimental and predicted results of 64%. Using a modified validation process designed to investigate the predictivity of a more focused SAR model, the system was able to achieve a concordance of 71% between experimental and predicted results. Among the major biophores identified by CASE/MULTICASE as associated with cancer causation in rats, several are derived from electrophilic or potentially electrophilic compounds (e.g., aromatic amines, nitrogen mustards, isocyanates, epoxides). Other biophores however are derived from chemicals seemingly devoid of actual or potential DNA-reactivity and as such may represent structural features of non-genotoxic carcinogens.

Identification of `genotoxic' and `non-genotoxic' alerts for cancer in mice: the carcinogenic potency database

A set of chemicals tested for carcinogenicity in mice that have been analyzed by Gold et al. [L.S. Gold, C.B. Sawyer, R. Magaw, G.M. Backman, M. deVeciana, R. Levinson, N.K. Hooper, W.R. Havender, L. Bernstein, R. Peto, M.C. Pike, B.N. Ames, Environ. Health Perspect. 58 (1984) 9–319; L.S. Gold, M. deVeciana, G.M. Backman, M. Lopipero, M. Smith, R. Blumenthal, R. Levinson, L. Bernstein, B.N. Ames, Environ. Health Perspect. 67 (1986) 161–200; L.S. Gold, T.H. Slone, G.M. Backman, R. Magaw, M. DaCosta, P. Lopipero, M. Blumenthal, B.N. Ames, Environ. Health Perspect. 74 (1987) 237–329; L.S. Gold, T.H. Slone, G.M. Backman, S. Eisenberg, M. DaCosta, M. Wong, N.B. Manley, L. Rohrbach, B.N. Ames, Environ. Health Perspect. 84 (1990) 215–286; L.S. Gold, N.B. Manley, T.H. Slone, T.H. Garfinkle, L. Rohrbach, B.N. Ames, Environ. Health Perspect. 100 (1993) 65–135] in the first five plots of the carcinogenic potency database (CPDB) was subjected to CASE/MULTICASE analyses. Briefly, CASE/MULTICASE is a computer-automated structure evaluation system that is capable of identifying structural features of chemicals associated with a specified biological activity (e.g., carcinogenicity or mutagenicity). These features are then incorporated into a structure-activity relationship (SAR) model for the analyzed database. The mouse CPDB used in this study consists of 627 chemicals, 289 of which are carcinogens, 11 marginal or weak carcinogens (i.e., chemicals requiring high doses to induce cancer) and 327 non-carcinogens. In an internal prediction analysis where the CASE/MULTICASE SAR model was used to predict the carcinogenicity of chemicals used to create the model, a concordance between experimental and predicted results of 96% was obtained. This indicates that the model is able to satisfactorily explain the chemicals in the learning set. In a drop-one cross-validation study where chemicals were removed one at a time and the remaining n−1 chemicals were used in an iterative method to create a model to predict the removed chemical, CASE/MULTICASE was able to achieve a concordance between experimental and predicted results of 70%. Using a modified validation process designed to investigate the predictivity of a more focused SAR model, the system achieved a 78% concordance between experimental and predicted results. Among the major biophores identified by CASE/MULTICASE associated with cancer causation in mice several are derived from electrophilic or potentially electrophilic compounds (e.g., hydrazines, N-mustards, N-nitrosamines, aromatic amines, reactive halogens, and quinones). Other biophores however are derived from chemicals seemingly devoid of actual or potential DNA-reactivity and as such may represent structural feature of non-genotoxic carcinogens.

Structural determinants associated with risk of human developmental toxicity

OBJECTIVES: Identifying drugs or chemicals that represent hazards to human development is a continuous challenge. Of the approximately 60,000 chemicals in commercial use only 5% have been evaluated for developmental toxicity. Identification of inexpensive, rapid, validated techniques to demonstrate chemical hazards for the human embryo or fetus is the objective of this research. STUDY DESIGN: This research explored identification of structure activity predictors associated with human developmental toxicity by means of MULTICASE (multiple computer-automated structure evaluation), an algorithm that evaluates associations between chemicals and their constituent fragments and a biologic response. This algorithm allows identification of chemicals (and specific substructures) that may be human developmental toxicants. Developmental toxicity data were compiled from two sources (the Teratogen Information System and Food and Drug Administration guidelines) and analyzed to identify structural determinants (biophores) associated with human developmental toxicity. RESULTS: This analysis identified 17 biophores associated with human developmental toxicity. Testing the biophores against the learning set demonstrated 99% concordance, 100% sensitivity, and 98% specificity. Cross-validation studies were conducted, in which the original database was randomly separated into five learning and test sets; these demonstrated a mean concordance of 73%, with a mean sensitivity of 63% and a mean specificity of 79%. CONCLUSIONS: The MULTICASE structure-activity model is useful for identifying potential human developmental toxicants, as well as serving as a starting point for mechanistic investigations. (Am J Obstet Gynecol 1997;176:799-806.)

Prediction of the carcinogenicity of a second group of organic chemicals undergoing carcinogenicity testing.

Twenty-four organic compounds currently undergoing testing within cancer bioassays under the aegis of the U.S. National Toxicology Program (NTP) were submitted to the computer automated structure evaluation (CASE) and multiple computer automated structure evaluation (MULTICASE) system for predictions of activity. Individual predictions resulting from the NTP combined rodent, NTP mouse, Carcinogenic Potency Database (CPDB) combined rodent, and CPDB mouse databases were combined using Bayes' theorem to yield an overall probability of rodent carcinogenicity. Based upon an arbitrary probability cut-off of 0.50, nine compounds were predicted to be rodent carcinogens. The predicted carcinogens are chloroprene, 1-chloro-2-propanol, codeine, emodin, furfuryl alcohol, isobutyraldehyde, primaclone, sodium xylenesulfonate, and t-butylhydroquinone.

Computational and molecular modeling evaluation of the structural basis for tubulin polymerization inhibition by colchicine site agents

The computer-automated structure evaluation programs MultiCASE and CASE were used to perform a quantitative structure-activity relationship study on tubulin polymerization inhibitors. A learning set of 536 chemicals (202 active, 27 marginal, and 307 inactive), built using IC 50 values for inhibition of tubulin polymerization or mitosis from this and previous studies, was used for artificial intelligence self-teaching. The algorithms successfully predicted the activity of agents in the learning set with > 90% accuracy. Seventeen MultiCASE and twelve CASE (mostly included in the MultiCASE set) biophores (substructures significantly correlated with activity) were identified with a probability > 0.95. Here we present the biophores of podophyllotoxins, colchicinoids, and certain combretastatins, each examined for structure-activity relationships. For the podophyllotoxins and colchicinoids in the learning set, the correlations between observed and predicted potencies were > 0.85. The algorithms recognized the importance of several known site, electronic, and steric effects in the two classes. A predictive QSAR ( R 2 = 0.98) was developed for combretastatin A-2 and dihydrocombretastatin analogues. The MultiCASE/CASE analyzes were used in combination with molecular models to study relative orientations of colchicine, podophyllotoxin, combretastatin A-4, and steganacin at the colchicine site. This resulted in a new hypothesis, consistent with extensive published experimental data, in which the C-ring and part of the B-ring of colchicine overlap with the A- and B-rings of podophyllotoxin. Consequently, the trimethoxyphenyl rings of colchicine and podophyllotoxin occupied different regions of space, each pointing out from a hydrophobic ‘core’ occupied by the overlapping biophores. The molecular model of the highly potent combretastatin A-4 could fit into the model binding site in at least three different ways. The developed QSARs were used to identify the potent microtubule stabilizer discodermolide. Its identification, in concert with recently reported findings, suggest potential overlap in the colchicine and paclitaxel binding sites on tubulin.

Computer-aided study of the relationship between structure and antituberculosis activity of a series of isoniazid derivatives

Abstract The Multiple Computer Automated Structure Evaluation (MultiCASE) program was used to analyze the relationship between the structure and antituberculous activity of a series of 136 hydrazides, most of them isoniazid related. The structural features revealed by this analysis are discussed. The most significant one seemed to be the distance between the pyridinic nitrogen and the terminal nitrogen of the hydrazido group. Given the affiliation of these two heteroatoms with a planar conjugated system, we suggest that Schiff base chemistry similar to that of vitamin B 6 may be involved in the mode of action of isoniazid and its related compounds. A mechanism of action of isoniazid is proposed and suggestions for the design of new isoniazid-type drugs are made.

Developmental toxicity of carboxylic acids to Xenopus embryos: a quantitative structure-activity relationship and computer-automated structure evaluation.

The developmental toxicity for each of 45 carboxylic acids was determined for Xenopus embryos. Acids tested included 12 unbranched, saturated aliphatics, 12 branched, saturated aliphatics, 12 unsaturated aliphatics, and 9 aromatics. Embryos were collected following hormone-induced breeding and exposed to at least eight concentrations of the acid, along with a control. For each concentration, 25 properly developing embryos were exposed to the acid solution for 96 h. Each acid was tested on at least three separate occasions and the data were pooled to calculate 96-h LC50 (lethality), 96-h EC50 (malformation), and DHI (developmental hazard index = 96-h LC50/96-h EC50) values. The endpoint data were subjected to quantitative structure-activity relationship (QSAR) analyse: and computer-automated structure evaluation (CASE). Variation in acid-induced lethality was effectively explained by partitioning and ionizability of the acids, while partitioning alone was somewhat effective in explaining variation for acid-induced malformation. The results indicated that developmental hazard of the acids to Xenopus embryos is primarily dependent on carbon-chain length, with acids containing five carbon atoms in the chain tending to be the most potent. Unsaturation reduced the hazard in comparison with the corresponding unbranched saturated acid. Developmental hazard was highest for 2-position branched compounds with a 5- or 6-C chain, but was reduced for 2-position branched acids with a 3- or 4-C chain. Hazard of the non-2 position branched acids was variable. Valproic (2-propylpentanoic) acid showed the highest developmental hazard with Xenopus, twice that of any other acid tested.

A computerized connectivity approach for analyzing the structural basis of mutagenicity in Salmonella and its relationship with rodent carcinogenicity.

We have applied a new software program, based on graph theory and developed by our group, to predict mutagenicity in Salmonella. The software analyzes, as information in input, the structural formula and the biological activities of a relatively large database of chemicals to generate any possible molecular fragment with size ranging from two to ten nonhydrogen atoms, and detects (as predictors of biological activity) those fragments statistically associated with the biological property investigated. Our previous work used the program to predict carcinogenicity in small rodents. In the current work we applied a modified version of the program, which bases its predictions solely on the most important fragment present in a given molecule, considering as practically negligible the effects of additional less important fragments. For Salmonella mutagenicity we used a database of 551 compounds, and the program achieved a level of predictivity (73.9%) comparable to that obtained by other authors using the Computer Automated Structure Evaluation (CASE) program. We evaluated the relative contributions of biophores and biophobes to overall predictivity: biophores tended to be more important than biophobes, and chemicals containing both biophores and biophobes were more difficult to predict. Many of the molecular fragments identified by the program as being strongly associated with mutagenic activity were similar to the structural alerts identified by the human experts Ashby and Tennant. Our results tend to confirm that structural alerts useful to predict Salmonella mutagenicity are generally not very strong predictors of rodent carcinogenicity. Although the predictivity level achieved for oncogenic activity improved when the program was directly trained with carcinogenicity data, carcinogenicity as a biological endpoint was still more difficult to predict than Salmonella mutagenicity.

Developmental toxicity of carboxylic acids to Xenopus embryos: A quantitative structure‐activity relationship and computer‐automated structure evaluation

The developmental toxicity for each of 45 carboxylic acids was determined for Xenopus embryos. Acids tested included 12 unbranched, saturated aliphatics, 12 branched, saturated aliphatics, 12 unsaturated aliphatics, and 9 aromatics. Embryos were collected following hormone-induced breeding and exposed to at least eight concentrations of the acid, along with a control. For each concentration, 25 properly developing embryos were exposed to the acid solution for 96 h. Each acid was tested on at least three separate occasions and the data were pooled to calculate 96-h LC50(lethality), 96-h EC50 (malformation), and DHI (developmental hazard index = 96-h LC50/96-h EC50) values. The endpoint data were subjected to quantitative structure-activity relationship (QSAR) analyses and computer-automated structure evaluation (CASE). Variation in acid-induced lethality was effectively explained by partitioning and ionizability of the acids, while partitioning alone was somewhat effective in explaining variation for acid-induced malformation. The results indicated that developmental hazard of the acids to Xenopus embryos is primarily dependent on carbon-chain length, with acids containing five carbon atoms in the chain tending to be the most potent. Unsaturation reduced the hazard in comparison with the corresponding unbranched saturated acid. Developmental hazard was highest for 2-position branched compounds with a 5- or 6-C chain, but was reduced for 2-position branched acids with a 3- or 4-C chain. Hazard of the non-2-position branced acids was variable. Valproic (2-propylpentanoic) acid showed the highest developmental hazard with Xenopus, twice that of any other acid tested. © 1996 Wiley-Liss, Inc.

The influence of the extent of target organs on sensitivities of methods for screening rodent carcinogens

Two hundred and thirty-three rodent carcinogens from the Carcinogenic Potency Database (CPDB) were analyzed with CASE (Computer Automated Structure Evaluation), and a comparison of the extents of target organs with the sensitivities for long-term carcinogenic bioassays in rats and mice, Salmonella assay (Sty), electrophilic substructure alert analysis (ESAA) and CASE was made. The carcinogenicity of 233 chemicals was evaluated in both rat and mouse bioassays. The present study showed that the sensitivities of the five methods for screening carcinogens were related to the extents of target organs of carcinogens. Among the carcinogens that did not induce tumors (extent = 0) in rats, the sensitivities of Sty and ESAA were 46 and 53, respectively. Among the carcinogens which induced tumors at a single organ (extent = 1) in rats, the sensitivities were 57 and 64 respectively; and 71 and 80 at multiple organs (extent>1) respectively. The sensitivities of CASE were 76, 82, and 89 respectively at these three different extents. Similar results were obtained with these carcinogens in mice. The results indicate that mutagenic or electrophilic carcinogens are more likely to induce tumors at multiple target organs; in contrast, most carcinogens which induced tumors at only a single target organ in one species are rarely mutagenic or electrophilic. The sensitivities of Sty and ESAA were lower than that of the CASE method in these carcinogens. CASE analyzed chemical structures of many carcinogens and non-carcinogens and then established a database of key fragments, and its parameters are not only based on mutagenicity or electrophilicity of chemicals, and this resulted in a more exact detection of the carcinogenicity of chemicals with the CASE method.

Quantitative structure-agonist activity relationship of capsaicin analogues

The MULTIple Computer Automated Structure Evaluation (MULTICASE) methodology has been used to study the quantitative structure-agonist activity relationship of a series of capsaicin agonists. A number of substructures and physiochemical properties of capsaicin analogues were identified as being responsible for high agonist potency. The optimal log P value for the agonist potency as estimated from QSAR analysis is 5.12. It was also found that a cluster of inactive molecules in the database have lipophilicity values below 2.94. Molecular modeling was employed to elucidate the detailed structural features of the pharmacophore of capsaicin analogues. Systematic conformational analysis has shown that the activity of capsaicin analogues strongly depends upon their ability to reach the required conformational profile. Based upon these observations, a three-dimensional pharmacophore model for the capsaicin-receptor interactions is proposed.

Application of the multiple computer automated structure evaluation methodology to a quantitative structure–activity relationship study of acidity

AbstractWe applied the Multiple Computer Automated Structure Evaluation (Multi‐CASE) program to the analysis of the relationship between the structure of 2464 organic acids and their (first) pKa values. By using the self‐created expert dictionary of molecular attributes pertinent to acidity, the program could make successful a priori prediction of the acidity of new organic compounds. © 1994 by John Wiley & Sons, Inc.