A Support Vector Machine Classification Model for Benzo[c]phenathridine Analogues with Topoisomerase-I Inhibitory Activity

1. Introduction

Figure 1. Chemical structures of benzo[c]phenanthridine derivatives.

Figure 1. Chemical structures of benzo[c]phenanthridine derivatives.

2. Results and Discussion

2.1. Feature Selection

Three feature selection approaches, namely mRMR (Max-Relevance, Min-Redundancy), GA (genetic algorithm) and RF (random forest) were applied to the dataset to figure out a set of chemical descriptors related to bioactivity properties from 2,032 molecular descriptors calculated by Dragon. Based on the calculation, three sets of molecular descriptors were selected, including a set of 10, 16 and seven descriptors for the mRMR, GA and RF methods, respectively. Table 1 shows the molecular descriptors selected via the RF method. After that, these sets of descriptors were used to develop the SVM models. For any SVM, it is necessary to select the optimal parameters of the Kernel function (C, γ) and these values for each set of descriptors were figured out and are shown in Table 2. The results of classification based on three different descriptor sets (by mRMR, GA and RF) by the SVM algorithm (the e1071 package in R) are shown in Table 3. The total accuracies of these SVM models are greater than or equal to 0.91 on training sets and are greater than or equal to 0.74 for cross-validation. However, the basic principle of SVM is a supervised learning approach. Hence, the classification results regarding test set and external set showed the relatively accurate performance of models. The SVM model which was developed from descriptors selected by RF method gave a better classification power than the two other methods.

Table 1. Molecular descriptors from RF feature selection method.

Table 1. Molecular descriptors from RF feature selection method.

Class	Symbol	Definition
Topological descriptors	D/Dr05	distance/detour ring index of order 5
	D/Dr06	distance/detour ring index of order 6
Walk and path counts	MPC06	molecular path count of order 06
	MPC08	molecular path count of order 08
	MPC10	molecular path count of order 10
2D frequency fingerprints	F05[C–C]	frequency of C–C at topological distance 05
	F08[N–O]	frequency of N–O at topological distance 08

Table 2. Optimal parameters (C, γ) for SVM approach.

Table 2. Optimal parameters (C, γ) for SVM approach.

Feature selection method	Number of features selected	Range of γ	Best γ	Range of C	Best C	Cross-validation error
mRMR	10	5[−10:10]	3.125	5[−10:10]	1	0.21
GA	16	2[−10:10]	0.125	2[−10:10]	16	0.15
RF	7	2[−10:10]	0.25	2[−10:10]	4	0.17

Table 3. Classification results of 3 SVM models corresponding with 3 descriptor sets selected successively via mRMR, GA and RF methods.

Table 3. Classification results of 3 SVM models corresponding with 3 descriptor sets selected successively via mRMR, GA and RF methods.

Feature selection method	Training set			Test set			External set
	mRMR	GA	RF	mRMR	GA	RF	mRMR	GA	RF
Number of support vectors	51	40	35
Total accuracy	0.98	0.91	0.93	0.80	0.93	0.87	0.67	0.78	0.89
Sensitivity	0.93	0.73	0.87	0.33	1.00	1.00	0.40	0.60	0.80
Specificity	1.00	0.98	0.95	0.92	0.92	0.83	1.00	1.00	1.00
Positive precision	1.00	0.92	0.87	0.50	0.75	0.60	1.00	1.00	1.00
Negative precision	0.98	0.91	0.95	0.85	1.00	1.00	0.57	0.67	0.80
Matthews correlation cofficient (MCC)	0.96	0.77	0.82	0.29	0.83	0.71	0.48	0.63	0.80
Total accuracy of cross-validation	0.76	0.78	0.74

2.2. SVM Classification Model

A total of 82 compounds were used for SVM and classified into actives or inactives based on the the relative effective concentration (REC) compared to topotecan and the respective threshold points. For the separation of active/inactive, REC of topotecan (REC = 1) was selected as a threshold point. TOP-I active compound having equal or stronger activity than that of topotecan is presented as “1” and vice versa, “0” is presented TOP-I inactive compound having weaker activity than that of topotecan. The set of molecular descriptors selected via RF method was used to create the final SVM (the e1071 package in R) classification model based on anti-topoisomerase-1 activity. Moreover, two other classification approaches namely the SVM kernlab and “randomForest” (RF) in R are also applied and the classification results are presented in Table 4. In general, both of SVM packages (the e1071 package, the package kernlab) gave better results than those of observed for random forest methods with the different values in the total accuracy of 20%.

Table 4. Classification results from three classified approaches.

Table 4. Classification results from three classified approaches.

	Training set			Test set			External set			Application set
Evaluation criteria	SVM e1071	SVM Kernlab	RF	SVM e1071	SVM kernlab	RF	SVM e1071	SVM kernlab	RF	SVM e1071
TP	13	13	6	3	3	3	4	3	3	5
TN	41	41	36	10	8	10	4	4	3	3
FP	2	2	9	0	0	0	1	2	2	0
FN	2	2	7	2	4	2	0	0	1	2
Total accuracy	0.93	0.93	0.72	0.87	0.73	0.87	0.89	0.78	0.67	0.80
Sensitivity	0.87	0.87	0.46	0.60	0.43	0.60	1.00	1.00	0.75	0.71
Specificity	0.95	0.95	0.80	1.00	1.00	1.00	0.80	0.67	0.60	1.00
Positive precision	0.87	0.87	0.40	1.00	1.00	1.00	0.80	0.60	0.60	1.00
Negative precision	0.95	0.95	0.84	0.83	0.67	0.83	1.00	1.00	0.75	0.60
MCC	0.82	0.82	0.25	0.71	0.54	0.71	0.80	0.63	0.35	0.65
Cross-validation error	0.22	0.20	0.28 a
Y-scrambling total accuracy	0.59

^a “out of bags” of RF method is based on principle of cross-validation.

Regarding the training set, two SVM packages (e1071, kernlab) expressed similar classification power. However, regarding the test set and the external set, the SVM package e1071 showed the better results. The Matthews correlation coefficient (MCC) of this package was stable for training, test and external test sets with values 0.82, 0.71 and 0.80, respectively. For the kernlab package, the MCC value for the training set was as high as 0.82 whereas the MCC of the test set was only 0.54. Normally, a model with MCC larger than 0.4 indicates that it has the predictive power [18]. The RF classification method in combination with a set of descriptors chosen by RF gave MCC = 0.35 on the external set and this result indicates that the RF classification model has no ability to predict the biological activity of compounds in this study. The SVM-e1071 in R classification model with a set of descriptors identified from a large set via a RF algorithm showed the best results.

To further validate the use of SVM-e1071 for TOP-I classification, a 50-fold Y-scrambling procedure for the training sets was performed [19,20,21]. The total accuracy of 0.59 was obtained from the Y-scrambling analyses. The results showed that our models show a significantly better performance than those obtained when class assignments are randomly achieved with total accuracy values of 0.93 versus 0.59, respectively. However, it has to be noted that the principle of SVM is supervised learning and the learning strategy tries to keep the error to a minimum values which explains the relative “good” performance of this method for the y scrambled data sets.

2.3. Validation and Application

The final SVM model was validated not only by cross-validation procedure but also by an external dataset (not belonging to the dataset used to create the SVM classification model). The power for classification of BCPs by anti-TOP-I activity was 0.87–0.93 for total accuracy and 0.22 for cross-validation error. Moreover, the final SVM model was applied to classify a set of 10 BCP analogues recently synthesized by Lavoie et al., which so-called application set or external test set 2 and detailed chemical structures and TOP-I activities are shown in Table 5 [22,23,24,25,26]. According to literature, among these 10 BCP analogues, seven compounds have stronger anti-TOP-I activity than topotecan and three compounds are weaker than topotecan. The results indicated that the classification model was achieved a correct prediction of 80% (8/10) and the detailed results are presented in Table 4. The positive accuracy gained the value of 100% i.e., the classification model is more accurate for predicting compounds wtith stronger activity than topotecan.

Table 5. Chemical structure often benzo[c]phenanthridine derivatives in application set and their topoisomerase I inhibitory activity REC and classification results from final SVM model. Classification term: “1” presented TOP-I active compound having equal or stronger activity than that of topotecan; “0” presented TOP-I inactive compound having weaker activity than that of topotecan.

Table 5. Chemical structure often benzo[c]phenanthridine derivatives in application set and their topoisomerase I inhibitory activity REC and classification results from final SVM model. Classification term: “1” presented TOP-I active compound having equal or stronger activity than that of topotecan; “0” presented TOP-I inactive compound having weaker activity than that of topotecan.

No	Chemical structure	Name	REC TOP-I mediated DNA cleavage (Experimental)	TOP-I Active/Inactive	TOP-I classified result from final SVM model
A1		BMC_08_7824_7a	0.03	1	1
A2		BMC_08_7824_7b	0.08	1	1
A3		BMC_08_7824_9	0.2	1	1
A4		BMC_08_7824_11	0.1	1	1
A5		BMC_08_8598_9	>10	0	0
A6		BMC_08_8598_10	0.2	1	0
A7		BMC_08_8598_12	0.2	1	0
A8		BMC_08_8598_13	0.2	1	1
A9		BMC_08_8598_14	>10	0	0
A10		BMC_08_8598_15	>10	0	0

This result completely meets our goal, which is aiming to look for new inhibitors of TOP-I for cancer chemotherapy. However, one limitation of all machine learning approaches is their inability to indicate the important role of functional groups related to biological activity. Hence, the combination of this SVM classification model with molecular docking studies [27,28] and also related 2D- and 3D-QSAR model on cytotoxicity of BCPs [5] could provide insight into the molecular basis of TOP-I inhibitors.

3. Experimental

3.1. Dataset

BCP analogues in the study (82 compounds) with topoisomerase I inhibitory activity evaluated by testing of DNA cleavage were collected from the work of LaVoie et al. [6,7,8,9,10,11,12,13,14,15,16,17]. The biological activity data is represented by the relative effective concentration (REC) compared to topotecan whose value is arbitrarily set at 1.0 (as reference compound). This parameter allows the comparison of molecules based on the cleavage of plasmid DNA in the presence of human topoisomerase I. Therefore, with a compound having the REC value higher than 1, that means its topoisomerase I inhibitory activity lower than topotecan and vice versa. Based upon the REC values, the data set was grouped into two classes: 58 analogues were assigned to inactive class (REC value > 1, negatives or inactives) and 24 analogues were classified as active class with REC value ≤ 1 (high TOP-I blockade, positives or actives). Chemical structure of 82 benzo[c]phenanthridine derivatives and their topoisomerase I inhibitory activity REC values were presented in Table 6.

Table 6. Chemical structure of82 benzo[c]phenanthridine derivatives and their topoisomerase I inhibitory activity REC and classification results from final SVM model. Classification term: “1” presented TOP-I active compound having equal or stronger activity than that of topotecan; “0” presented TOP-I inactive compound having weaker activity than that of topotecan.

Table 6. Chemical structure of82 benzo[c]phenanthridine derivatives and their topoisomerase I inhibitory activity REC and classification results from final SVM model. Classification term: “1” presented TOP-I active compound having equal or stronger activity than that of topotecan; “0” presented TOP-I inactive compound having weaker activity than that of topotecan.

No	Chemical structure	Name	REC TOP-I mediated DNA cleavage (Experimental)	TOP-I Active/Inactive	TOP-I classified result from final SVM model
1		Nitidine	10	0	0
2		BMC_03_3795_10a	8	0	0
3		BMC_03_3795_10b	200	0	0
4		BMC_03_3795_10c	200	0	0
5		BMC_03_3795_10d	>1000	0	0
6		BMC_03_3795_10e	500	0	0
7		BMC_03_3795_10f	10	0	0
8		BMC_03_3795_11a	>1000	0	0
9		BMC_03_3795_11b	100	0	0
10		BMC_03_3795_12d	>1000	0	0
11		BMC_03_2061_03a	0.5	1	0
12		BMC_03_2061_03b	>1000	0	0
13		BMC_03_2061_03c	0.3	1	1
14		BMC_03_2061_03d	1000	0	0
15		BMC_03_2061_03e	1	1	0
16		BMC_03_2061_03f	1000	0	0
17		BMCL_02_3333_03c	10	0	0
18		BMC_03_2061_03h	50	0	0
19		BMC_03_2061_03i	1	1	0
20		BMC_03_2061_03j	>1000	0	0
21		BMC_03_2061_03k	0.8–1.0	1	1
22		BMC_03_2061_04a	0.8	1	0
23		BMC_03_2061_04b	100	0	1
24		BMC_03_2061_09k	10	0	0
25		JMC_03_2254_02	0.3	1	0
26		JMC_03_2254_03	6	0	0
27		JMC_03_2254_05a	1000	0	1
28		JMC_03_2254_05b	0.1	1	1
29		JMC_03_2254_06a	15	0	0
30		JMC_03_2254_06b	0.5	1	0
31		JMC_03_2254_05c	0.2	1	0
32		JMC_03_2254_06c	8.0	0	0
33		JMC_03_2254_16a	10	0	1
34		JMC_03_2254_17a	500	0	0
35		JMC_03_2254_02	0.5	1	1
36		BMCL_02_3333_02	>1000	0	0
37		BMCL_02_3333_03	200	0	0
38		BMCL_02_3333_04a	0.3	1	1
39		BMCL_02_3333_04b	1000	0	0
40		BMCL_02_3333_04c	30	0	0
41		BMCL_02_3333_04d	1.0	1	0
42		LDDD_04_198_01	0.03	1	0
43		LDDD_04_198_02	2.0	0	0
44		LDDD_04_198_03	2.0	0	0
45		BMC_04_3731_03a	9	0	0
46		BMC_04_3731_03b	6	0	0
47		BMC_04_3731_03c	2	0	0
48		BMC_04_3731_03d	>300	0	0
49		BMC_04_3731_04a	100	0	0
50		BMC_04_3731_04b	12	0	0
51		BMC_04_3731_04c	6	0	0
52		BMC_04_0795_04	>300	0	0
53		BMC_04_3731_05	10	0	0
54		BMC_04_0795_01b	0.3	1	0
55		BMC_04_0795_01f	0.2	1	1
56		BMC_04_0795_01g	2.0	0	0
57		BMC_04_0795_01h	20	0	0
58		BMC_04_0795_03	10	0	0
59		BMC_04_0795_02	>1000	0	0
60		BMC_04_0795_04	0.8	1	1
61		BMC_04_0795_05	5	0	0
62		BMC_05_6782_07c	1.0	1	0
63		BMC_05_6782_07d	8	0	0
64		BMC_05_6782_08	0.6	1	1
65		BMC_05_6782_09c	0.4	1	0
66		BMC_05_6782_09d	10	0	0
67		BMC_05_6782_09e	0.7	1	1
68		BMC_05_6782_09f	12	0	0
69		BMC_05_6782_09g	60	0	0
70		BMC_05_6782_09h	0.6	1	1
71		BMC_05_6782_09i	1.5	0	0
72		BMC_05_6782_09j	0.3	1	1
73		BMC_05_6782_10	3	0	1
74		BMC_06_3131_10c	1.2	0	0
75		BMC_06_3131_10d	0.07	1	1
76		BMC_06_3131_10g	9	0	0
77		BMC_06_3131_10i	0.45	1	1
78		BMC_06_3131_10j	>100	0	0
79		BMC_06_3131_10f	3	0	0
80		BMC_06_3131_10k	13	0	0
81		BMC_06_3131_10l	0.35	1	1
82		BMC_06_3131_10m	0.15	1	1

3.2. Training and Test Sets

The training and test sets were generated by random division. Firstly, a set of nine compounds was selected and not used to develop the models. These compounds were separated from the others and considered as external set. The remaining analogues were split randomly for five times into 80% for training sets and 20% for test sets using the R software [30,31]. The numbers of compounds in each subset are presented in Table 7.

Table 7. BCPs datasets division.

Table 7. BCPs datasets division.

Dataset	Total compounds	Actives a	Inactives b
Whole set	82	24	58
Training set	58	15	43
Test set	15	5	10
External set	9	4	5
Application set (External set 2)	10	7	3

^a Actives: Compounds whose activity is equal or stronger than topotecan; ^b Inactives: compounds whose activity is weaker than topotecan.

3.4. Support Vector Machine

Support vector machine (SVM), the most successfully applied new classification algorithms, were introduced to the machine learning community by Vapnik. Smola and Schölkopf provided an extensive tutorial on SVMs [30,31,32,33,34]. The underlying idea of an SVM classifier is to map linearly inseparable input data into a higher dimensional space where the data can be linearly separated, using a maximal separating hyperplane. Support Vector Machines (SVM) is the classification system based on the supervised learning approach. In this study, SVM algorithm in the e1071 package in R with Kernel function was used [30,31]. The strategies for the SVM classification model are shown in Figure 2.

Selection of optimal parameters of Kernel function: RBF Kernel function has two parameters (C and γ) and the selection of these parameters is one of two critical issues to develop the good SVM model (the other is the feature selection) [35,36,37]. To find the optimal parameters, an algorithm Grid (function tune in R) is made following the process described in Figure 3. The training set and test set are used to find a pair of optimal parameters (C and γ) of the Kernel function. Pairs of parameters were tested in intervals reduced step by step an algorithm Grid. The pair is chosen when the error of cross validation is minimal [30,34].

Figure 2. Process of SVM classification model.

Figure 2. Process of SVM classification model.

Figure 3. Selection of optimal parameters of Kernel function using Grid algorithm.

Figure 3. Selection of optimal parameters of Kernel function using Grid algorithm.

3.5. Evaluation Criteria for Classification Model

Performance of the SVM models was measured by using standard parameters for classification models [18,19,38,39,40]. They are described as follows:

• (i) The overall classification accuracy of a prediction model, accuracy = (tp + tn)/(tp + fp + tn + fn),
• (ii) Sensitivity (recall, accuracy on actives) = tp/(tp + fn),
• (iii) Specificity (accuracy on inactives) = tn/(tn + fp),
• (iv) Precision on actives = tp/(tp + fp),
• (v) Precision on inactives = tn/(tn + fn) and,
• (vi) Matthews correlation coefficient (MCC):

The quality of a classification model is generally measured by using these parameters, which are estimated for the whole set and by applying a cross-validation protocol based on a leave-one-out (LOO) procedure. In all equations, tp = number of true positives, tn = number of true negatives, fp = number of false positives, and fn = number of false negatives.

4. Conclusions

References and Notes

1. Fleury, F.; Sukhanova, A.; Ianoul, A.; Devy, J.; Kudelina, I.; Duval, O.; Alix, A.J.P.; Jardillier, J.C.; Nabiev, I. Molecular determinants of site-specific inhibition of human DNA topoisomerase I by fagaronine and ethoxidine. Relation to DNA binding. J. Biol. Chem. 2000, 275, 3501–3509. [Google Scholar]
2. Pommier, Y. DNA topoisomerase I inhibitors: Chemistry, biology, and interfacial inhibition. Chem. Rev. 2009, 109, 2894–2902. [Google Scholar] [CrossRef]
3. Nakanishi, T.; Suzuki, M.; Mashiba, A.; Ishikawa, K.; Yokotsuka, T. Synthesis of NK109, an anticancer benzo[c]phenanthridine alkaloid. J. Org. Chem. 1998, 63, 4235–4239. [Google Scholar]
4. Li, T.K.; Houghton, P.J.; Desai, S.D.; Daroui, P.; Liu, A.A.; Hars, E.S.; Ruchelman, A.L.; LaVoie, E.J.; Liu, L.F. Characterization of ARC-111 as a novel topoisomerase I-targeting anticancer drug. Cancer Res. 2003, 63, 8400–8407. [Google Scholar]
5. Liao, S.Y.; Qian, L.; Lu, H.L.; Shen, Y.; Zheng, K.C. A combined 2D- and 3D-QSAR study on analogues of ARC-111 with antitumor activity. QSAR Comb. Sci. 2008, 27, 740–749. [Google Scholar] [CrossRef]
6. Li, D.; Zhao, B.; Sim, S.-P.; Li, T.-K.; Liu, A.; Liu, L.F.; LaVoie, E.J. 2,3-Dimethoxybenzo(i)-phenanthidines: Topoisomerase I-targeting anticancer agents. Bioorg. Med. Chem. 2003, 11, 521–528. [Google Scholar] [CrossRef]
7. Yu, Y.; Singh, S.K.; Liu, A.; Li, T.K.; Liu, L.F.; LaVoie, E.J. Substituted dibenzo(c,h)cinnolines: Topoisomerase I-targeting anticancer agents. Bioorg. Med. Chem. 2003, 11, 1475–1491. [Google Scholar] [CrossRef]
8. Makhey, D.; Li, D.; Zhao, B.; Sim, S.-P.; Li, T.-K.; Liu, A.; Liu, L.F.; LaVoie, E.J. Substituted benzo(i)phenanthridines as mammalian topoisomerase I-targeting anticancer agents. Bioorg. Med. Chem. 2003, 11, 1809–1820. [Google Scholar] [CrossRef]
9. Ruchelman, A.L.; Singh, S.K.; Ray, A.; Wu, X.H.; Yang, J.-M.; Li, T.-K.; Liu, A.; Liu, L.F.; LaVoie, E.J. 5H-Dibenzo(c,h)1,6-napthiridin-6-ones: Novel topo-isomerase I-targeting anticancer agents with potent cytotoxic activity. Bioorg. Med. Chem. 2003, 11, 2061–2073. [Google Scholar] [CrossRef]
10. Li, D.; Zhao, B.; Sim, S.-P.; Li, T.-K.; Liu, A. 8,9-Methylenedioxybenzo-(i)phenanthridines: Topoisomerase I-targeting activity and cytotoxicity. Bioorg. Med. Chem. 2003, 11, 3795–3805. [Google Scholar] [CrossRef]
11. Ruchelman, A.L.; Singh, S.K.; Ray, A.; Wu, X.; Yang, J.M.; Zhou, N.; Liu, A.; Liu, L.F.; LaVoie, E.J. 11H-Isoquino(4,3-c)cinnolin-12-ones: Novel anticancer agents with potent topoisomerase I-targeting activity and cytotoxicity. Bioorg. Med. Chem. 2004, 12, 795–806. [Google Scholar]
12. Ruchelman, A.L.; Kerrigan, J.E.; Li, T.-K.; Zhou, N.; Liu, A.; Liu, L.F.; LaVoie, E.J. Nitro and amino substitution within the A-ring of 5H-8,9-dimethoxy-5-(2-N,N-dimethylamino-ethyl)dibenzo(c,h)(1,6)naph-thyridin-6-ones: Influence on topoisomerase I-targeting activity and cytotoxicity. Bioorg. Med. Chem. 2004, 12, 3731–3742. [Google Scholar]
13. Zhu, S.; Ruchelman, A.L.; Zhou, N.; Liu, A.; Liu, L.F.; LaVoie, E.J. Esters and amides of 2,3-dime-thoxy-8,9-methylene-dioxybenzo(i)phenanthridine-12-carboxylic acid: Potent topoisomerase I-targeting agents. Bioorg. Med. Chem. 2005, 13, 6782–6794. [Google Scholar] [CrossRef]
14. Zhu, S.; Ruchelman, A.L.; Zhou, N.; Liu, A.; Liu, L.F.; LaVoie, E.J. 6-Substituted 6H-dibenzo(c,h)(2,6)naph-thyridin-5-ones: Reversed lactam analogues of ARC-111 with potent topoisomerase I-targeting activity and cytotoxicity. Bioorg. Med. Chem. 2006, 14, 3131–3143. [Google Scholar] [CrossRef]
15. Ruchelman, A.L.; Singh, S.K.; Wu, X.; Ray, A.; Yang, J.M.; Li, T.-K.; Liu, A.; Liu, L.F.; LaVoie, E.J. Diaza- and triazachrysenes: Potent topoisomerase I-targeting agents with exceptional antitumor activity against the human tumor xenograf, MDA-MB-435. Bioorg. Med. Chem. Lett. 2002, 12, 3333–3336. [Google Scholar]
16. Ruchelman, A.L.; Zhu, S.; Zhou, N.; Liu, A.; Liu, L.F.; LaVoie, E.J. Dimethoxybenzo(i)-phenanthridine-12-carboxylic acid derivatives and 6H-dibenzo(c,h) (2,6)naphthyridin-5-ones with potent topoisomerase I-targeting activity and cytotoxicity. Bioorg. Med. Chem. Lett. 2004, 14, 5585–5589. [Google Scholar] [CrossRef]
17. Feng, W.; Satyanarayana, M.; Cheng, L.; Liu, A.; Tsai, Y.-C.; Liu, L.F.; LaVoie, E.J. Synthesis of N-substituted 5-[2-(N-alkylamino)ethyl]dibenzo[c,h][1,6]-naphthyridines as novel topoisomerase I-targeting anti-tumor agents. Bioorg. Med. Chem. 2008, 16, 9295–9301. [Google Scholar]
18. Yap, C.W.; Chen, Y.Z. Prediction of cytochrome P450 3A4, 2D6, and 2C9 Inhibitors and substrates by using support vector machines. J. Chem. Inf. Model. 2005, 45, 982–992. [Google Scholar] [CrossRef]
19. Thai, K.-M.; Ecker, G.F. Classification models for hERG inhibitors by counter-propagation neural networks. Chem. Biol. Drug Des. 2008, 72, 279–289. [Google Scholar] [CrossRef]
20. Kubinyi, H. Validation and predictivity of QSAR models. In QSAR & Molecular Modelling in Rational Design of Bioactive Molecules, Proceedings of the 15th European Symposium on QSAR & Molecular Modelling, Istanbul, Turkey, 2004; Sener, E.A., Yalcin, I., Eds.; CADDD Society: Ankara, Turkey, 2006; pp. 30–33. [Google Scholar]
21. Gramatica, P. Principles of QSAR models validation: Internal and external. QSAR Comb. Sci. 2007, 26, 694–701. [Google Scholar] [CrossRef]
22. Feng, W.; Satyanarayana, M.; Tsai, Y.-C.; Liu, A.A.; Liu, L.F.; LaVoie, E.J. 11-Substituted 2,3-dimethoxy-8,9-methylenedioxybenzo[i]phenanthridine derivatives as novel topoisomerase I-targeting agents. Bioorg. Med. Chem. 2008, 16, 8598–8606. [Google Scholar] [CrossRef]
23. Satyanarayana, M.; Tsai, Y.-C.; Liu, A.A.; Liu, L.F.; LaVoie, E.J. Syntheses and biological evaluation of topoisomerase I-targeting agents related to 11-[2-(N,N-dimethylamino)ethyl]-2,3-dimethoxy-8,9-methylene-dioxy-11H-isoquino[4,3-c]cinnolin-12-one (ARC-31). Bioorg. Med. Chem. 2008, 16, 7824–7831. [Google Scholar] [CrossRef]
24. Satyanarayana, M.; Tsai, Y.-C.; Liu, A.A.; Liu, L.F.; LaVoie, E.J. Synthesis of N-substituted 5-[2-(N-alkyl-amino)ethyl]-dibenzo[c,h][1,6]-naphthyridines as novel topoisomerase I-targeting antitumor agents. Bioorg. Med. Chem. 2008, 16, 9295–9301. [Google Scholar] [CrossRef]
25. Scaglioni, L.; Mazzini, S.; Mondelli, R.; Dallavalle, S.; Gattinoni, S.; Tinelli, S.; Beretta, G.L.; Zunino, F.; Ragg, E. Interaction between double helix DNA fragments and a new topopyrone acting as human topoisomerase I poison. Bioorg. Med. Chem. 2009, 17, 484–491. [Google Scholar]
26. Satyanarayana, M.; Tsai, Y.-C.; Liu, A.A.; Liu, L.F.; LaVoie, E.J. 12-Substituted 2,3-dimethoxy-8,9-methylenedioxybenzo-[i]phenanthridines as novel topoisomerase I-targeting antitumor agents. Bioorg. Med. Chem. 2009, 17, 2877–2885. [Google Scholar] [CrossRef]
27. Clark, R.L.; Deane, F.M.; Anthony, N.G.; Johnston, B.F.; McCarthy, F.O.; Mackay, S.P. Exploring DNA topoisomerase I inhibition by the benzo[c]phenanthridines fagaronine and ethoxidine using steered molecular dynamics. Bioorg. Med. Chem. 2007, 15, 4741–4752. [Google Scholar]
28. Khadka, D.B.; Cho, W.J. 3-Arylisoquinolines as novel topoisomerase I inhibitors. Bioorg. Med. Chem. 2011, 19, 724–734. [Google Scholar] [CrossRef]
29. Muegge, I.; Oloff, S. Advances in virtual screening. Drug Dis. Today Tech. 2006, 3, 405–411. [Google Scholar] [CrossRef]
30. The R Project for Statistical Computing. Available online: http://www.r-project.org (accessed on 26 December 2011).
31. Karatzoglou, A.; Meyer, D.; Hornik, K. Support vector machines in R. J. Stat. Softw. 2006, 15, 1–28. [Google Scholar]
32. Tetko, I.V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.; Ertl, P.; Palyulin, V.A.; Radchenko, E.V.; Zefirov, N.S.; Makarenko, A.S.; et al. Virtual computational chemistry laboratory—Design and description. J. Comput. Aid. Mol. Des. 2005, 19, 453–463. [Google Scholar] [CrossRef]
33. VCCLAB, Virtual Computational Chemistry Laboratory. Available online: http://www.vcclab.org (accessed on 30 January 2010).
34. Hsu, C.-W.; Chang, C.-C.; Lin, C.-J. LIBSVM: A Practical Guide to Support Vector Classification. Available online: http://www.csie.ntu.edu.tw/~cjlin/libsvm (accessed on 5 November 2010).
35. Fröhlich, H.; Chapelle, O.; Schölkopf, B. Feature selection for support vector machines by means of genetic algorithms. In. In Proceeding ICTAI '03, Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, Sacramento, CA, USA, 3–5 November 2003; IEEE Computer Society: Washington, DC, USA, 2003; pp. 142–148. [Google Scholar]
36. Fröhlich, H.; Chapelle, O.; Schölkopf, B. Feature selection for support vector machines using genetic algorithms. Int. J. Artif. Intell. T. 2004, 13, 791–800. [Google Scholar] [CrossRef]
37. Demel, M.A.; Janecek, A.G.K.; Thai, K.-M.; Ecker, G.F.; Gansterer, W.N. Predictive QSAR models for polyspecific drug targets: The importance of feature selection. Curr. Comput. Aided Drug Des. 2008, 4, 91–110. [Google Scholar] [CrossRef]
38. Thai, K.-M.; Ecker, G.F. A binary QSAR model for classification of hERG potassium channel blockers. Bioorg. Med. Chem. 2008, 16, 4107–4119. [Google Scholar] [CrossRef]
39. Thai, K.-M.; Ecker, G.F. Similarity-based SIBAR descriptors for classification of chemically diverse hERG blockers. Mol. Divers. 2009, 13, 321–336. [Google Scholar] [CrossRef]
40. Ji, L.; Wang, X.; Qin, L.; Luo, S.; Wang, L. Predicting the androgenicity of structurally diverse compounds from molecular structure using different classifiers. QSAR Comb. Sci. 2009, 28, 542–550. [Google Scholar] [CrossRef]