Shengang Yuan

SVM approach for predicting LogP

SummaryThe logarithm of the partition coefficient between n-octanol and water (logP) is an important parameter for drug discovery. Based upon the comparison of several prediction logP models, i.e. Support Vector Machines (SVM), Partial Least Squares (PLS) and Multiple Linear Regression (MLR), the authors reported SVM model is the best one in this paper.

Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines

The study of prediction of toxicity is very important and necessary because measurement of toxicity is typically time-consuming and expensive. In this paper, Recursive Partitioning (RP) method was used to select descriptors. RP and Support Vector Machines (SVM) were used to construct structure–toxicity relationship models, RP model and SVM model, respectively. The performances of the two models are different. The prediction accuracies of the RP model are 80.2% for mutagenic compounds in MDL’s toxicity database, 83.4% for compounds in CMC and 84.9% for agrochemicals in in-house database respectively. Those of SVM model are 81.4%, 87.0% and 87.3% respectively.

CoMFA/CoMSIA/HQSAR and Docking Study of the Binding Mode of Selective Cyclooxygenase (COX‐2) Inhibitors

Abstract The intermolecular interaction between four types of anti‐inflammatory inhibitors (oxazoles, pyrazoles, pyrroles and imidazoles) and COX‐2 receptor was studied. The results of docking suggest that they have similar interaction mechanism. The most active compounds of these four types of inhibitors could both form several hydrogen bonds with residues His90, Arg513, Leu352 and Arg120, and develop hydrophobic interaction with residues Phe518, Leu352 and Leu359. This is consistent with the investigation reported by R. G. Kurumbail et al. (Nature. 1996, 384, 644‐648). A common 3D‐QSAR model could be constructed with these four categories of COX‐2 inhibitors using the method of docking‐ guided conformer selection. The cross‐validated q2 values are found as 0.741 and 0.632 for CoMFA and CoMSIA respectively. And the non‐cross‐validated r2 values are 0.887 and 0.885. 54 inhibitors constitute the test set used to validate the model. The results show that this model possesses good predictive ability for diverse COX‐2 inhibitors. Furthermore, a HQSAR model was used to evaluate the influence of substituents on anti‐inflammatory activity. Compared with the results of previous works, our model possesses significantly better prediction ability. It could help us to well understand the interaction mechanism between inhibitors and COX‐2 receptor, and to make quantitative prediction of their inhibitory activities.

CISOC-PSCT: a predictive system for carcinogenic toxicity

A SAR based carcinogenic toxicity prediction system, CISOC-PSCT, was developed. It consisted of two principal phases: the construction of relationships between structural descriptors and carcinogenic toxicity indices, and prediction of the toxicity from the SAR model. The training set included 2738 carcinogenic and 4130 non-carcinogenic compounds. Three predefined topological types of substructures termed Star, Path and Ring were used to generate the descriptors for each structure in the training set. In this system, the defined carcinogenic toxicity index (CTI) was obtained from the probability of a structural descriptor to either belong to the carcinogenic or non-carcinogenic compounds. Based on these structural descriptors and their CTI, a SAR model was derived. Then the carcinogenic possibility (CP) and the carcinogenic impossibility (CIP) of compounds were predicted. The model was tested from a testing set of 304 carcinogenic compounds (MDL toxicity database), 460 non-carcinogenic compounds (CMC database) and 94 compounds extracted from two traditional Chinese medicine herbs.

Comparative study of non nucleoside inhibitors with hiv-1 reverse transcriptase based on 3D-QSAR and docking

The intermolecular interaction between two types of non nucleoside reverse transcriptase inhibitors (NNRTIs), HEPT and TIBO, and HIV reverse transcriptase receptor (HIVRT) was investigated. The result of docking study showed that two types of NNRTIs presented similar interaction mechanism with HIVRT. The most active compound of every type of inhibitors could form one hydrogen bond with the residue Lys101 and has hydrophobic interaction with residues Tyr181, Tyr188 and Tyr318, etc. Three 3D-QSAR models including two partial correlation models (one for each family of HEPT and TIBO) and a mixed model gathering two families were constructed. Comparative study of these models indicated that the mixed model offered the strongest prediction ability. For this model, the cross-validated q 2 values were 0.720 and 0.675, non-cross-validated r 2 values were 0.940 and 0.920 for CoMFA and CoMSIA, respectively. It has been validated by using a test set of 27 inhibitors. Compared with previously reported works, our model showed better prediction ability. It could help us to insight the interaction between NNRTIs and HIVRT, and to design new anti-HIV NNRTIs inhibitors.

New Strategy of Mass Spectrum Simulation Based on Reduced and Concentrated Knowledge Databases

Abstract A new strategy for mass spectrum simulation is proposed based on the analysis of different reported techniques and developed software packages. This new strategy consists of using four pivot knowledge databases: functional groups, fragmentation pathways, end‐point and pseudo end‐point fragments, and peak‐intensity relationships. The key database is the cleavage mode database, which was constructed by data mining of large mass spectra databases. An important advantage of this concentrated database is its rich information but largely reduced size compared with the databases used in other reported systems. Based on this new strategy, a mass spectrum simulation system, MASSIS, was developed, and the performance was evaluated by comparing the simulated spectra with experimental data for a large population of molecules. The results show that this system possesses high performance. The average ratio of simulated spectra with respect to experimental ones is superior to 90% for all compounds taken together. The reported mass spectral simulation system in this paper could be the first general software for organic chemistry use. This paper was invited as a contribution to a special issue of the journal concerning Chemical Spectroscopy in China. It was presented at Sichuan University, Chengdu China, August 1–7, 2004; the 4th Conference for Worldwide Young Chinese Chemists.

Construction of a generic reaction knowledge base by reaction data mining

As synthesis by combinatorial chemistry and high throughput screening have become well-established strategies in the drug discovery process, chemists face increased challenges in managing large amounts of data and using these data to design more diverse and focused libraries. As synthesis is an intuitive and empirical process, however, the classical approaches to computer-assisted synthesis planning do not fully satisfy the needs of the synthetic chemist. We describe a novel computational technique for extracting reaction data and building a generic reaction knowledge base (GRKB) to provide chemists with useful and well-organized knowledge. The method consists of three key steps: (1) the automatic recognition of reaction centers, (2) the definition of a hierarchy of reaction patterns, and (3) the organization of the generic reaction knowledge. Significant reaction knowledge has been discovered via mining a subset of the InfoChem Reaction database. A frame system has been constructed to store and retrieve the GRKB. Applications of this GRKB to synthesis planning are illustrated.

An Effective Topological Symmetry Perception and Unique Numbering Algorithm

Determination of equivalence classes of atoms in molecules and the unique numbering for the molecular graphs are of major interest for many structure processing tasks and many programs have been reported for this purpose. Most of them were based on the use of graph invariants, but such methods reportedly failed to give correct partitioning for certain structures and the only theoretically rigorous method is based on atom-by atom matchings which was considered to be computationally impractical. In order to avoid the failures of partitioning and the time-consuming atom-by-atom matching, on the basis of a profound analysis on the mechanism of Morgan algorithm, this work proposed two improvements for the original morgan algorithm. The first improvement is to avoid the oscillatory behavior of Morgan algorithm. The second improvement referred to as single-beter Morgan algorithm, is to decompose the Morgan algorithm into single-vertex processing. By incorporating these improvements, an effective topological symmetry perception and unique numbering algorithms were devised. The high performance of these algorithms is demonstrated with some graphs that are difficult to partition.

MASSIS: A Mass Spectrum Simulation System. 1. Principle and Method:

A mass spectrum simulation system was developed. The simulated spectrum for a given target structure is computed based on the cleavage knowledge and statistical rules established and stored in pivot databases: cleavage rule knowledge, functional groups, small fragments and fragment-intensity relationships. These databases were constructed from correlation charts and statistical analysis of a large population of organic mass spectra using data mining techniques. Since 1980, several systems have been proposed for mass spectrum simulation, but at present there is no commercial software available. This shows the complexity and difficulties in the development of such a system. The reported mass spectral simulation system in this paper could be the first general software for organic chemistry use.

Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking

An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.