FungiPAD(Fungicide Physicochemical-properties Analysis Database) is a special database to not only provide information about fungicides but also qualitative and quantitative evaluate fungicide-likeness of small molecules. We have collected information on all approved 377 fungicides. CAS numbers, common names, 3D structures and physicochemical properties for these molecules are recorded in our database. This website also allows you to compute physicochemical descriptors and fungicide-likeness scores of small molecules to support fungicide discovery.

Fig 1. The Architecture of FungiPAD

2.Browser Recommendation

We tested our database using different browsers on different systems (IE10 or later on Windows, Firefox on Windows, Mac OS and Linux, Google Chrome on Windows, Mac OS, and Linux, Apple Safari on Windows and Mac os) to assure the normal display. The testing results showed good compatibility.

3.Browse Method

You can browse three classes of fungicides respectively. Structures and physicochemical properties of these molecules directly displayed in the Browse page, click ID-link to view more basic information, including common name, IUPAC name, CAS number, smiles, inchi, physicochemical properties, bioavailability radars and fungicide-likeness scores.

Fig 2. Browse FungiPAD

5.Analysis Method

Predicting the fungicide-likeness of experimental molecules is important in the discovery process. Our database can help you to analysis the fungicide-likeness of your compounds. The input is an user-defined structure generated by JSME. Your selected compounds with related physicochemical properties and their distribution histograms, bioavailability radars and fungicide-likeness scores will be showed in a few minute. You can evaluate the fungicide-likeness of compounds by the Radar (the pink area represents the optimal range for each properties) and histograms at a first glance or according to the fungicide-likeness scores ( the higher the score, the better the fungicide-likeness of your compounds). These evaluating methods are not meant to be as accurate as virtual screening tools, but the results are indicators of compounds showing desirable fungicide-likeness physicochemical properties.

Fig 4. Analysis FungiPAD

6.Score Function Building

We establish quantitative assessments (QEF, RDL and Gau) to efficient prioritize fungicide-likeness compounds. Every approach relies on a small number of molecular descriptors which are relevant, accessible and easy to compute to describe the distribution of a set of molecules.

1.QEF accurately describe six molecular properties(MW, LogP, HBA, HBD, nRotB, arR) over the fungicides, parameterized by a,b,c,o coefficients computed for each distribution of fungicides properties. And the individual dfi (i molecular descriptor) was joined by computing geometric means and logarithm.

2.Gaussian function describes a more multivariate descriptors' distribution to ascertain fungicide-likeness of small molecule compounds, including MW, LogP, HBA, HBD, PSA, nRing, nN, nO and nRotB. The values Xi represent the nine physicochemical properties respectively. The parameters μi and σi represent the average and standard deviation of each Xi.

3.Following bayesian probability theory, we randomly selected 1000 compounds from the ChEMBL database considered as a negative set, and fungicides as positive sets to build RDL function. P(X|D) is the probability of the property X given that a compound is a drug. P(X|D') the probability of the property X given that a compound not being a drug. We considered six properties(MW, LogP, HBA, HBD, nRotB, arR) used in QEX.