to overcome the above - described limitations , the present inventors have tested the ability of computational neural networks to predict binding free energy . neural networks are employed to investigate the properties of substrate and inhibitor molecules . through training algorithms that are described below , the network can find properties and areas of molecules that are necessary for biological recognition , binding and action . the molecules are described using general quantum mechanical descriptions of the substrate and inhibitor molecules , and so the algorithm in a real sense “ chooses its own qsar ”. because the method focuses on substrates and inhibitors , and because these molecules are small enough for application of standard quantum chemistry molecular orbital calculations , there is no ambiguity as to choice of force fields , dielectric constants , or classical versus quantum mechanics . in addition , complex problems associated with the binding of water and counter ions in active sites are also avoided . these constructions have been shown to be able to satisfy all the requirements for flexibility described above . that is , they are able to discern from input data the specific data crucial for forming the proper conclusion ; and from the data are able to extrapolate to a relationship between input and output . in the present work , the electrostatic potential at the van der waals surface of a molecule is used as the physicochemical descriptor . the entire surface for each molecule , represented by a discrete collection of points , serves as the input to the neural network . to preserve the geometric and electrostatic integrity of the training molecules , a collapse onto a lower dimensional surface is avoided . after alignment of the inhibitor molecule for maximal geometrical overlap with the transition state structure , the electrostatic potentials on the inhibitor surface are mapped onto the van der waals surface of the transition state . therefore , though an inhibitor molecule takes on the geometry of the transition state , the electrostatic potentials decorating that surface are derived from the inhibitor itself . the molecular electrostatic potential calculated at the van der waals surface of the molecules is used as a descriptor of chemical structure and properties . such information sheds light on the kinds of interactions a given molecule can have with the active site . regions with electrostatic potentials close to zero are likely to be capable of van der waals interactions . regions with a partial positive or negative charge can serve as hydrogen bond donor or acceptor sites . regions with even greater positive or negative potentials may be involved in coulombic interactions . the electrostatic potential also conveys information concerning the likelihood that a particular region can undergo electrophilic or nucleophilic attack . the electrostatic potential surfaces are quantified as follows . after a constrained energy minimization of a molecular structure using the gaussian 94 package ( gaussian 94 , revision c . 2 , gaussian , inc . pittsburgh , pa . ), its cube function is used to calculate the electron density and electrostatic potential . since molecules described by quantum mechanics have a finite electron density in all space , a reasonable cutoff is required to define a molecular geometry . one can closely approximate the van der waals surface by finding all points around a molecule where the electron density is close to 0 . 002 ± δ electron / bohr . δ is the acceptance tolerance since no gaussian output will have an electron density of 0 . 002 exactly . the set of points thus generated will describe a surface under which approximately 95 % of the electron density resides . δ is adjusted so that 17 points per atom are accepted , creating a fairly uniform molecular surface as shown previously ( bagdassarian et al ., j . amer . chem . soc . 118 : 8825 - 8836 ( 1996 )). the information about a given molecular surface is described by a matrix with dimensions of 4 × n where n is the number of points for the molecule , and the row vector of length 4 contains the x , y , z - coordinates of a given point and the electrostatic potential there . for input of the surface features of the structures into a neural network the molecules must be oriented for maximum geometric coincidence . this can be done in either of two ways . in the first , the molecular stick figures are superimposed via , for example , the algorithms provide in the insight ii package ( biosym technologies , san diego , calif .). one selects the obvious atoms from two molecules that are to coincide spatially , which is a simple matter for molecules sharing a great deal of backbone similarity . once the superposition is achieved , the gaussian calculations are performed with the nosymm feature to preserve the spatial orientations of the molecules . only then are the surfaces constructed from the electrostatic potential and electron density outputs . the second way to achieve properly oriented surfaces is as follows . once the van der waals surfaces of the molecules have been constructed through the procedure described above , two molecules are spatially positioned with their geometric centers at the coordinate origin . one molecule is held fixed while the other is rotated around its center , and for each new position a geometric similarity measure s g is used to gauge the degree of alignment : s g = ∑ i = 1 n   a  ∑ j = 1 nb  exp  ( - α   r ij 2 ) ∑ i = 1 n   a  ∑ j = 1 n   a  exp  ( - α   r ij 2 )  ∑ i = 1 n   b  ∑ j = 1 n   b  exp  ( - α   r ij 2 ) the double sum in the numerator is over all surface points on molecule a and on molecule b . na and nb refer to the number of surface points in molecule a and b , respectively . r 2 ij is the spatial distance squared between point i on a and j on b . α is the length scale that weighs the degree to which spatial distances between i and j affect s g . the denominator is a normalizing factor . s g is calculated for many random orientations , and the relative orientation with the maximum s g is saved as it corresponds to the orientation of molecule a with respect to b with maximal surface coincidence . all molecules are thus oriented to a reference target molecular surface — that of the transition state . input patterns entering into a neural network are presented in the form of a vector with entries ( l 1 , l 2 , . . . , l n ). since the molecules are represented by a 4 × n matrix , a method is needed to discard the x , y , z - coordinates but maintain the electrostatic potentials while preserving the maximum amount of spatial information . this is accomplished by mapping the surface points of every molecule onto the same geometrical surface , such as , for example , that defined by the transition state ( bagdassarian et al ., int . j . quant . chem . ; quant . biol symp . 23 : 73 - 80 ( 1996 )). in order to represent different molecules to the neural network , with their differences and similarities preserved , a nearest neighbor mapping function for the surface points is used . to accomplish this mapping , the molecular surfaces must be oriented for maximum geometric coincidence , as described above , and each inhibitor molecule is mapped onto the transition state . for each point on the transition state surface the spatially closest point on the inhibitor surface is found and the electrostatic potential of that inhibitor point is assigned the coordinates of that transition state point . therefore the transition state , substrate , and inhibitors are all represented by the same geometrical surface , that of the transition state . however , the electrostatic potentials on these surface points defining a particular molecule are derived by the projection of the electrostatic potentials of that original molecular surface . after the molecules are described with the same geometry , input vectors are created with only the electrostatic potential information , ignoring the positional information since it is now the same for all molecules . this mapping ensures that similar regions on different molecules enter the same part of the neural network . this mapping assumes that the shape of the transition state is matched by the cavity at the active site . this cavity is responsible for formation of the transition state . the value of this approach will be shown a posteriori by the results . each data point in the input — that is , each discrete point chosen on the van der waals surface at which the electrostatic potential is evaluated — enters the neural network through a discrete neuron . the network is composed of many simple neurons acting in parallel . the network function is determined by the interaction between these neurons . networks “ learn ” by adjusting the strength of interaction between the neurons . the network has an input layer , a hidden layer , and an output layer . in the input layer , each input neuron corresponds to an input datum ( in this case , a point on the van der waals surface and the associated electrostatic potential or a deviation of a geometric location from a reference surface ). there is complete interconnection between all neurons in adjacent layers , and the strength of interconnection is what is varied in training of the network . the network used in the present invention was a feed forward network with back propagation of error that learns with momentum . training of the network is accomplished by repeated backpropagation of error throughout the network . each iteration involves the introduction of an input and an output pattern , calculation of error , and readjustment of internal parameters called weights and biases . a generalization of the widrow - hoff learning rule was used to modify interconnection weights until presentation of the network with an electrostatic potential input pattern resulted in output of a known binding free energy . after the network is trained , it may be presented with an unknown pattern and it will mathematically generalize to produce an output binding free energy . the number of input patterns required to train a network varies with the input data ; however , as reported below , between 4 and 7 training inhibitors were sufficient to allow a neural network to produce accurate predictions for unknown inhibitors . few input substrates are required because each input contains hundreds of data points , and therefore a great deal of information . the small number of input substrates is remarkable from a mathematical perspective , and makes the method very practical to use . the basic construction of a back propagation neural network has three layers : an input layer , hidden layer , and an output layer ( fig3 ). the input layer is where the different input vectors are transferred . the link between the layers of the network is one of multiplication by a weight matrix , where every entry in the input vector is multiplied by a weight and sent to every hidden layer neuron , so that the hidden layer weight matrix has the dimensions n by m , where n is the length of the input vector and m is the number of hidden layer neurons . a bias is added to the hidden and output layer neurons , which scales all the arguments before they are input into the transfer function . the hidden layer input h l j for neuron j is calculated , h j l = b j + ∑ i = 1 n  i j o  w ij where i o i is the output from the i th input neuron , w ij is the element of the weight matrix connection input from neuron i with hidden layer neuron j , and b j is the bias on the hidden layer neuron j . this vector h l j is sent through a transfer function , f this function is nonlinear and usually sigmoidal , taking any value and returning a number between − 1 and 1 . a typical example is : f  ( h j l ) = 1 1 + e - h j l - 1 = h j o the hidden layer output , h o j is then sent to the output layer . the output layer input o l k is calculated for the k th output neuron o k l = b k + ∑ j = 1 m  h j o  w jk where w jk is the weight matrix element connecting hidden layer neuron j with output layer neuron k . the output layer output , o o k , is calculated with the same transfer function given above . referring to fig3 the input layer is represented by the squares at the top of the diagram . the weights are represented by the lines connecting the layers : w ij is the weight between the i th neuron of the input layer and j th neuron of the hidden layer and w jk is the weight between the j th neuron of the hidden layer and the k th neuron of the output layer . in this diagram the output layer has only one neuron because the target pattern is a single number — the free energy of binding . only a single output neuron is needed if the target for each input vector is a single number . back propagation was created by a generalization of the widrow - hoff learning rule applied to multiple - layer networks and nonlinear differentiable transfer functions ( rumelhart et al ., parallel distributed processing , vol . 1 , mit press , 1986 ). input vectors and the corresponding output vectors are used to train until the network can approximate a function . the strength of a back propagation neural network is its ability to form internal representations through the use of a hidden layer of neurons . for example , the ‘ exclusive or ’ problem demonstrates the ability of neural networks , with hidden layers , to form internal representations and to solve complex problems . suppose we have four input pattens [( 0 , 1 ) ( 0 , 0 ) ( 1 , 0 ) ( 1 , 1 )] with output targets [ 1 , 0 , 1 , 0 ], respectively . a perceptron or other single layer system would be unable to simulate the function described by these four input / output pairs . the only way to solve this problem is to learn that the two types of inputs work together to affect the output . in this case the least similar inputs cause the same output , and the more similar inputs have different outputs . the computational ability required to solve this problem is not unlike that required to find the best inhibitor when it does not look like the transition state . it is this inherent ability of neural networks to solve complex puzzles that makes them well conditioned for the task of simulating biological molecular recognition . we report a study of these techniques using a number of different enzyme systems with their respective transition states , substrates , and inhibitors . many different neural network constructions were studied , and the best neural network architecture varies with the enzyme system . variations in the number of hidden layer neurons often caused the greatest change in the ability of the network to learn , and between four and twelve hidden layer neurons were used . changing the number of iterations between 5 × 10 3 and 1 × 10 6 also had an affect on the ability of a network to learn . the learning rate controls the rate of change of the weights and biases , affecting a network &# 39 ; s ability to converge ; values ranging between 0 . 1 and 0 . 5 were used . a momentum term between 0 . 8 and 0 . 9 increased the probability that the network will converge at the global error minimum instead of a local error minimum . the success of the trained neural networks in predicting δg / rt for enzyme - inhibitor and enzyme - transition state interactions for amp nucleosidase and adenosine deaminase are shown in table 1 . the second column reports the experimentally determined free energies of binding for the transition states , substrates , or inhibitors shown in the first column . in the third column , the measure s e is used to rank similarity to the transition states . the fourth column gives predictions of δg / rt based on the s e values of the molecules . for these calculations , the binding free energies of the transition states and of the substrates are defined by the experimental values . predicted values of δg / rt based on the se values for the inhibitors were made by linear extrapolation between the values for the transition states and the substrates . the training procedure for the neural network for these two systems involved training the network with four patterns for each system . the number of hidden layer neurons , number of iterations , learning rate , and momentum were adjusted until the network output the binding energies of the four molecules in the training set with 98 % accuracy . once the network had learned the four patterns in the training set , it was used to predict a binding energy for the fifth molecule . these are the numbers listed in the fifth column of table 1 . for enzyme systems with few members in the training sets , the number of hidden layers , iterations , learning rate , and momentum that give the best predictions for the test molecules were optimized for the four patterns in each training set . for amp nucleosidase , the errors in δg / rt as predicted by se are : 0 . 0 for the transition state ( by construction ), 1 . 0 ( 6 %) for formycin , 2 . 0 ( 17 %) for aminopyrazolo pyrimidine ribonucleotide , 3 . 1 ( 36 %) for tubercidin , and 0 . 0 for amp ( again , by construction ). for the neural network predictions the following errors are found : 6 . 0 ( 15 %) for the transition state , 0 . 0 for formycin , 3 . 0 ( 25 %) for the aminopyrazolo pyrimidine ribonucleotide , 0 . 6 ( 6 %) for tubercidin , and 1 . 0 ( 11 %) for substrate . for adenosine deaminase , the errors in δg / rt as predicted by se are : 0 . 0 for the transition state ( by construction ), 2 . 0 ( 7 %) for hydrated purine ribonucleoside , 6 . 0 ( 24 %) for ( r )- coformycin , 11 . 0 ( 92 %) for 1 , 6 - dihydropurine ribonucleoside , and 0 . 0 for adenosine ( again , by construction ). for amp nucleosidase , the errors in δg / rt as predicted by the neural network are : 6 . 0 ( 15 %) for the transition state , 0 . 0 ( 0 %) for formycin , 3 . 0 ( 25 %) for aminopyrazolo pyrimidine ribonucleotide , 0 . 6 ( 6 %) for tubercidin , and 1 . 0 ( 11 %) for amp . for adenosine deaminase , the errors in δg / rt as predicted by the neural network are : 10 . 0 ( 26 %) for the transition state , 0 . 0 ( 0 %) for hydrated purine ribonucleoside , 9 . 0 ( 36 %) for ( r )- coformycin , 2 . 0 ( 17 %) for 1 , 6 - dihydropurine ribonucleoside , and 1 . 0 ( 10 %) for adenosine . for the three binding constants predicted by se , the average error is 20 % of the experimental δg / rt for amp nucleosidase , and 41 % for adenosine deaminase . even for such a small training set , the error from se is quite large in the case of adenosine deaminase , and this is mainly because 1 , 6 - dihydroribonucleoside is not a good inhibitor . without it , the average error is 15 %. the neural network , for the five predictions , performs with 11 % error in δg / rt in the case of amp nucleosidase , and 18 % error in the case of adenosine deaminase . the neural network is poorer at predicting the transition state binding free energy for adenosine deaminase . nonetheless , the average error over the five molecules in the adenosine deaminase series is only 18 %. both the neural network and the similarity measure have difficulty in predicting binding energy for ( r )- coformycin , because its ring structure is sufficiently different from the other molecules of the training set . cytidine deaminase catalyzes the hydrolysis of the amine group on cytidine to yield the products uridine and ammonia . besides the transition state for the reaction and the substrate , there were 10 other compounds available in the literature for which binding free energies had been measured ( betts et al ., j . mol . biol . 235 : 635 - 656 ( 1994 ); frick et al ., biochemistry 28 : 9423 - 9430 ( 1989 ); horensteinet al ., biochemistry32 : 7089 - 7097 ( 1993 )). fig4 shows the methyl derivatives ( replacing the ribose ring ) of the molecules used in these experiments . methyl derivatives were used because the ribose moiety is unchanged in all 12 molecules and so this constant factor remained the same for all potential inhibitors . there is no transition state structure available for this molecule , but there is a crystal structure available for the enzyme complexed with the transition state analog 5 - flouropyrimidin - 2 - one ribonucleoside , and this structure was used as a starting point for a transition state model . the reaction mechanism was assumed to be similar to that for adenosine deaminase . the c4 to o ( of the attacking — oh ) is constrained to be 1 . 67 å , corresponding to that found in the crystal structure of the enzyme - inhibitor complex . the remainder of the molecule was energy minimized , using the gaussian 94 package as described above . in this case , because we employ the methyl derivatives of the molecules , conformation about flexible bonds was not a significant factor in the calculations . there is another significant difference in the cytidine deaminase system with its more diverse set of inhibitors . in particular , the halogenated inhibitors represent a new challenge to the approach . in addition to changing the electrostatic features at the van der waals surface , the halogen substituted inhibitors differ in size so significantly from the transition state reference surface that geometric information needs to be included to derive the best results from the neural network approach . in addition to electrostatic information , the neural network was presented with a second set of data which gives the deviation of the surface points from a reference molecule chosen to be large enough that all other molecules were contained within its volume ( in particular , the 5 - bromo substituted surface ). the success of the proposed methodology is demonstrated by the results shown in table 2 . referring now to table 2 , the δg / rt values calculated by the neural network are shown using 7 and 11 molecules to train the network . in the case of 7 molecules , an actual experiment was simulated by choosing five molecules to leave out of the training set . these molecules were chosen without regard to chemical structure , but rather were chosen to span the range of binding free energy . of these five , one was chosen randomly to design the neural network architecture ( i . e ., the network &# 39 ; s adjustable parameters — number of hidden layer neurons , learning rate , momentum , and number of learning iterations ). these parameters were adjusted so that the approach could accurately predict the known binding free energy of the target molecule . no further adjustment to the network was made beyond this stage . finally this trained network was used to predict the binding free energies of the remaining four unknown molecules . as table 2 shows , the approach is able to predict the binding free energies , and is even able to yield reasonable results when trained with only 7 known experimental values , and when the network is optimized to predict the binding free energy of a randomly chosen molecule . to further test our approach , we have also investigated inhibitors for two different isoforms of nitric oxide synthetase ( nos ). there has been an explosion of interest in recent years in the biological importance of no , and its synthesis in living systems . the two isoforms studied are the brain isoform ( hereinafter “ bnos ”) and the inducible , ca 2 + - independent isoform ( hereinafter “ inos ”). these isoforms have very different roles under different biomedical scenarios . for example , overproduction of no by bnos during stroke has been implicated in cell death , and overproduction of inos has been implicated in circulatory shock and excess inflammation . consequently , selective inhibition of either isoform as appropriate would have significant potential medical benefits . in addition , potential inhibitors of nos vary widely in chemical structure ( fig5 ). four specific challenges are presented to the method of the present invention by the nos system . first , the great diversity in geometry of the inhibitors forces the method to show if widely variable geometric and electrostatic structures can all be handled by the same neural construction . second , the nos reaction is known to be an extraordinarily complex biochemical reaction , and so provides a rigorous test of the claim that predictions of binding free energies can be made by examining the quantum properties of substrate or inhibitor molecules . third , most of the molecules we study are linear chain molecules , predominantly connected by single bonds between atoms , and so have a high degree of conformational flexibility . this system provides a convincing test of the ability of the method to predict binding in such conformationally flexible systems when all molecules are held in an extended conformation . fourth , we have data available on a relatively large set of inhibitors for two different isoforms which not only exhibit quantitatively different binding energies , but qualitatively different binding patterns ( i . e ., the order of binding free energies shifts from one isoform to the other ). referring to fig5 the 18 molecules studied vary widely in structure , and in cases where clearly only the guanidino group was present ( or similarly an isothiourea ), the central carbon atom was aligned with the guanidino carbon of the arginine analogues . as in the cytidine deaminase study , a set of 12 randomly chosen molecules was used in the training set , along with a molecule with a known binding energy to optimize the construction of the neural network . then , the free energies of binding for the five remaining molecules were calculated . in each case , two sets of data are presented which represent two different choices of molecules for which predictions must be generated . in the second set molecule number 16 , the biotin derivative , was included in the prediction set because it is very different from the other molecules in the training set , and we felt it would be a strong test of the algorithm . the results for inos are shown in table 3 , and those for bnos are shown in table 4 . the data for inos are fairly uniform , with an average deviation of 1 . 58 and 1 . 75 dimensionless energy units . this level of accuracy is very surprising , given the complexity of the enzymatic reaction being tested . furthermore , the data support the application of this method to highly variable and flexible molecules with minimal information about binding . the data for bnos show slightly less absolute accuracy , but because the brain isoform binds inhibitors more tightly than inos , the relative error is only about 16 %, even in the worst case . the range of binding energies is greater than 10 dimensionless energy units for inos and 14 . 5 dimensionless energy units for bnos , so even with this highly variable and flexible set of inhibitor molecules , the results are accurate to within about 15 % of the binding energy range . the iu - nucleoside hydrolase system is involved in purine salvage by parasites from hosts . the enzyme was studied for two reasons . first , the mechanism of this enzyme is known to be very similar to that of the enzymatic subunit of cholera toxin . inhibitors of iu - nucleoside hydrolase are not inhibitors of cholera toxin , due to the presence of a dinucleotide as opposed to a mononucleotide . however , the ability to predict binding patterns in inhibitors of the iu - nucleoside hydrolase will permit the identification of possible variants of these inhibitors for testing as cholera toxin inhibitors . second , the existence of a large group of recently synthesized but uncharacterized inhibitors allowed a realistic test of the method ( see fig7 ). to train the neural network , an older set of 22 inhibitors of known binding free energy was used ( fig6 ). the binding constants of the molecules shown in fig7 were then calculated , and the results are presented in table 5 . accurate binding free energies could not be calculated for molecules which bind more weakly than ki = 50 μm . therefore , for several molecules , the experimental analysis could only show that the molecules bound at an absolute value less than 9 . 81 dimensionless units . however , the remainder of the data are in good agreement with the experimental data , with the worst errors being about 15 %. these results illustrate the utility of the method of the invention for identifying strong binders deserving of further study ( i . e ., those having δg / rt values less than − 14 ) from weak binders . the high level of predictive accuracy of neural networks makes it interesting to study how networks discriminate between different regions on the electrostatic potential surface of inhibitor molecules . the evident accuracy of the method is due to the use of the entire three dimensional surface of the molecule , rather than a collapsed representation . the weights in the hidden layer associated with regions of the input electrostatic surfaces that are important in binding have large absolute values . the network is presented with an input pattern and an output pattern . to minimize the error , the network must recognize regions that change and affect the binding energy relative to those regions that change and do not affect binding energy . this recognition occurs when the neural network &# 39 ; s weights are adjusted so that important regions are multiplied by large weights and unimportant regions are multiplied by small weights . documentation of this behavior is made by inspection of the absolute values of every number in the hidden - layer weight matrix of a trained network . the matrix is collapsed into a vector v i by summing on j where j = j , . . . , m and m is the number of hidden layer neurons : v i = ∑ j = 1 m   w ij  and where “ i ” refers to the input surface points . large values for v i represent regions found to be important to the neural network , and small values represent regions found to be unimportant . since all the patterns were generated by mapping the electrostatic potential of different molecules onto the transition state surface , the common transition state geometry is used to identify those regions on the molecules found as most important by the neural net . this can be represented by , for example , coloring points on a van der waals surface with large v i values one color and regions with small values another color . regions on molecular surfaces with intermediate weights have a mixture of the two colors . the network is not only able to identify regions in the training set that strengthen binding , it can also ignore regions that change without affecting binding . the entire molecular electrostaticpotential surfaces of the inhibitors , substrates , and experimentally determined transition states can be used to train neural networks to accurately predict binding energies of proposed inhibitor molecules . the neural network method possesses the ability to adjust a model of the system defined by a relatively small number of structure affinity pairs . our calculations show the ability of the method to predict an enzyme &# 39 ; s affinity to inhibitor molecules when minimal information about the enzymatic active site is provided . the predictive power extends to the tightly bound transition state when the network is trained with less tightly bound inhibitors . similar methods have been used by other groups for the task of simulating molecular recognition , but the present work is the first that uses the entire electrostatic potential surfaces of the molecules as the inputs to a back propagation neural network . our surface transformation has some similarity with the procedure of gasteiger et al . where kohonen self - organizing networks were used to transform different 3 - dimensional surfaces to a plane . the work of wagener et al . reduces the molecular electrostatic potential surfaces for each molecule onto twelve autocorrelation coefficients . importantly , these methods necessarily reduce the amount of information being used for prediction . wagener et al . report an investigation of the binding affinity between 32 molecules and a receptor site . because these molecules are constructed with similar steroid backbones , there is no confusion as to how to orient the molecules with respect to each other . the present method can be applied to this system as well . tetko et al . used a similar approach to that used by wagner et al . they devised a protocol that can be used to describe the structural features of molecules with a small set of coefficients . these sets of coefficients were used as inputs to a neural network . previous work with similarity measures give equal weight to all the regions of the molecular surfaces while neural networks become sensitive to certain regions and less sensitive to others . enzyme - substrate binding occurs through a number of specific interactions that do not cover the entire molecular surface . binding energy is not always a linear function of similarity to the transition state . neural networks can also learn to recognize regions of inhibitors likely to be chemically modified by an enzyme . the neural network method is well suited for the task of simulating biological molecular recognition . such methods can be used to search chemical libraries to augment the process of discovering pharmacological transition state inhibitors . it would be appreciated by those in the art that in addition to the electrostatic potential , other parameters descriptive of interactions between enzymes and substrates or inhibitors , and between receptors and ligands , could be utilized advantageously in the present method . these could include , for example , hydrophobic interactions , polarization effects , steric effects , and geometric effects . it would also be appreciated by those in the art that the present method can be encoded as information on a medium readable by a general - purpose computer to enable a computer to perform the necessary calculations . all patents and references mentioned hereinabove are hereby incorporated by reference in their entirety . while the foregoing invention has been described in some detail for purposes of clarity and understanding , it will be appreciated by one skilled in the art from a reading of the disclosure that various changes in form and detail can be made without departing from the true scope of the invention in the appended claims .