Researchers: Jean-Claude Bradley, Michael H Abraham, William E Acree, Jr., and Andrew SID Lang
All content, models and data are released as CC0

Objective

To recalculate the Abraham model solvent coefficients when requiring that the c-coefficient equal zero and then create and publish models predicting said coefficients. See model003 for background.

Procedure

The Abraham general solvation model uses the LFER

log P = c + e E + s S + a A + b B + v V

where c,e,s,a,b,v are the solvent coefficients and E,S,A,B,V are the solute descriptors. The Abraham coefficients are found via linear regression from measured data. The standard procedure is to allow the c-coefficient (the intercept) to float in the linear regression. We suggest that little predictive ability will be lost if we just require c to be zero. This will also allow easier comparison between solvents. Thus in order to compare both current solvents with each other and potential new solvents with current solvents, we decided to re-calculate the coefficients for known solvents e_0, s_0, a_0, b_0, v_0 by making c zero. This was achieved by calculating the log P values in over 90 solvents for ???? compounds with known Abraham descriptors from our ((figshare database?????)) and then re-running the linear regression using R. The following code with results is typical:

setwd(".../MakingCZero")
mydata = read.csv(file="makingczeroreadyforR.csv",head=TRUE,row.names="csid")
fit <- lm(isopropyl.myristate ~ 0 + E + S + A + B + V,data=mydata)
## summary of fit
summary(fit)
[output]
UPDATE BELOW.........................
Call:
lm(formula = isopropyl.myristate ~ 0 + E + S + A + B + V, data = mydata)
Residuals:
Min 1Q Median 3Q Max
-0.55191 -0.25598 -0.13732 0.00069 1.78549
Coefficients:
Estimate Std. Error t value Pr(>|t|)
E 0.977259 0.011781 82.95 <2e-16 ***
S -1.294959 0.014814 -87.41 <2e-16 ***
A -1.870114 0.020493 -91.26 <2e-16 ***
B -4.017729 0.015120 -265.73 <2e-16 ***
V 3.939081 0.007844 502.19 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2503 on 2139 degrees of freedom
Multiple R-squared: 0.9958, Adjusted R-squared: 0.9958
F-statistic: 1.009e+05 on 5 and 2139 DF, p-value: < 2.2e-16
[output]

The following table lists the original solvent coefficients together with the c=0 adjusted coefficients. Not surprisingly, the largest changes in coefficient values occur for solvents with c-values furthest away from zero. What is a little intriguing is that all the coefficients move consistently that same way.

UPDATE BELOW...............

That is, solvents with negative c-values all saw an increase in e and b (and a decrease in s,a, and v) when recalculation was performed, whereas solvents with positive c-values all saw an increase in s,a, and v (and decrease in e and b). By multiplying the average absolute deviation by the average descriptor value gives a measure of the degree by which the coefficients were changed. The adjusted coefficients changed (as measured by e.g. AAE(v_0) * Mean(V)) in the order v (0.124), s (0.043), e (0.013), b (0.011), a (0.010).

## Recalculating Solvent Coefficient

Researchers:Jean-Claude Bradley, Michael H Abraham, William E Acree, Jr., and Andrew SID LangAll content, models and data are released as CC0

## Objective

To recalculate the Abraham model solvent coefficients when requiring that the c-coefficient equal zero and then create and publish models predicting said coefficients. See model003 for background.## Procedure

The Abraham general solvation model uses the LFERlog P = c + e E + s S + a A + b B + v V

where c,e,s,a,b,v are the solvent coefficients and E,S,A,B,V are the solute descriptors. The Abraham coefficients are found via linear regression from measured data. The standard procedure is to allow the c-coefficient (the intercept) to float in the linear regression. We suggest that little predictive ability will be lost if we just require c to be zero. This will also allow easier comparison between solvents. Thus in order to compare both current solvents with each other and potential new solvents with current solvents, we decided to re-calculate the coefficients for known solvents e_0, s_0, a_0, b_0, v_0 by making c zero. This was achieved by calculating the log P values in over 90 solvents for ???? compounds with known Abraham descriptors from our ((figshare database?????)) and then re-running the linear regression using R. The following code with results is typical:

The following table lists the original solvent coefficients together with the c=0 adjusted coefficients. Not surprisingly, the largest changes in coefficient values occur for solvents with c-values furthest away from zero. What is a little intriguing is that all the coefficients move consistently that same way.

UPDATE BELOW...............

That is, solvents with negative c-values all saw an increase in e and b (and a decrease in s,a, and v) when recalculation was performed, whereas solvents with positive c-values all saw an increase in s,a, and v (and decrease in e and b). By multiplying the average absolute deviation by the average descriptor value gives a measure of the degree by which the coefficients were changed. The adjusted coefficients changed (as measured by e.g. AAE(v_0) * Mean(V)) in the order v (0.124), s (0.043), e (0.013), b (0.011), a (0.010).