2-Phenyl-1,4-benzopyrone 유도체(Flavones)의 Tyrosinase 저해활성에 관한 3D-QSARs 분석과 분자도킹

성낙도,박준호 한국응용생명화학회 2010 Journal of Applied Biological Chemistry (J. Appl. Vol.53 No.4

To understand the inhibitory activity with changing hydroxyl substituents (R1-R9) of polyhydroxy substituted 2-phenyl-1,4-benzopyrone analogues (1-25) against tyrosinase (PDB ID: oxy-form; 1WX2), molecular docking and the three dimensional quantitative structure-activity relationships (3D-QSARs: Comparative molecular field analysis (CoMFA) & Comparative molecular similarity indices analysis (CoMSIA)) were studied quantitatively. The statistically best models were CoMFA 1 and CoMSIA 1 model from the results. The optimized CoMSIA 1 model with the sensitivity of the perturbation and the prediction produced (dq2'/dr2yy'=1.009 & q2=0.511) by a progressive scrambling analysis were not dependent on chance correlation. The inhibitory activities with optimized CoMSIA 1 model were dependent upon electrostatic factor (51.4%) of substrate molecules. Contour mapping the 3D-QSAR models to the active site of tyrosinase provides new insight into the interaction between tyrosinase as receptor and 2-phenyl-1,4-benzopyrone analogues as inhibitor. Therefore, the results will be able to apply to the optimization of a new potent tyrosinase inhibitors. 기질분자로서 polyhydroxy 치환된 2-phenyl-1,4-benzopyrone 유도체(Flavones)(1-25)들의 hydroxyl 치환기(R1-R9)가 변화함에 따른 Tyrosinase(PDB ID: oxy-form; 1WX2)에 대한 저해활성을 이해하기 위하여 분자도킹과 3차원적인 정량적 구조-활성관계(3D-QSARs: CoMFA 및 CoMSIA)가 연구되었다. 그 결과, 통계적으로 CoMFA 1 및 CoMSIA 1 모델이 가장 양호한 3D-QSARs 모델이었다. 또한, 순차 혼합화 분석결과로부터 CoMSIA 1 모델(dq2'/dr2yy'=1.009 및 q2=0.511)이 우연상관성에 저촉되지 않는 최적화 모델이었으며 최적화된 CoMSIA 1 모델의 tyrosinase에 대한 저해활성은 기질분자의 정전기장(51.4%)에 의존적이었다. Tyrosinase의 반응점에 대한 3D-QSAR 모델의 등고도는 수용체로서 tyrosinase과 저해제로서 2-phenyl-1,4-benzopyrone 기질분자 사이의 새로운 상호작용 관계를 이해하는 계기가 되었다. 그러므로 이 결과들은 새로운 잠재적인 tyrosinase 저해제의 최적화에 적용될 수 있을 것이다.

3D 프린팅 소재 화학물질의 독성 예측을 위한 Data-centric XAI 기반 분자 구조 Data Imputation과 QSAR 모델 개발

유창규,정찬혁,김상윤,허성구,SHAHZEBTARIQ,신민혁 한국화학공학회 2023 Korean Chemical Engineering Research(HWAHAK KONGHA Vol.61 No.4

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

3D-QSAR Studies of 3,5-disubstituted Quinolines Inhibitors of c-Jun N-terminal Kinase-3

Madhavan, Thirumurthy The Basic Science Institute Chosun University 2011 조선자연과학논문집 Vol.4 No.3

c-Jun N-terminal kinase-3 (JNK-3) has been shown to mediate neuronal apoptosis and make the promising therapeutic target for neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and other CNS disorders. In order to better understand the structural and chemical features of JNK-3, comparative molecular field analysis (CoMFA) was performed on a series of 3,5-disubstituted quinolines derivatives. The best predictions were obtained CoMFA model ($q^2$=0.707, $r^2$=0.972) and the statistical parameters from the generated 3D-QSAR models were indicated that the data are well fitted and have high predictive ability. The resulting contour map from 3D-QSAR models might be helpful to design novel and more potent JNK3 derivatives.

3D-QSAR Analyses on the Inhibitory Activity of [(2-Phenylindol-3-yl)- methylene]propanedinitrile Analogues against Breast Cancer Cell and the Ligand Design of Active Molecules

Min-Gyu Soung,명평근,Nack-Do Sung 한국응용생명화학회 2009 Journal of Applied Biological Chemistry (J. Appl. Vol.52 No.1

Three-dimensional quantitative structure-activity relationships (3D-QSARs) on the inhibitory activity of [(2-phenylindol-3-yl)methylene]propanedinitrile analogues (1~19) against human breast cancer cells (MDA-MB 231) were studied using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The optimized CoMFA model 2 (r2 cv.(q2)=0.581, r2 ncv.=0.970) model predictability was lower than that of CoMSIA model 2, but showed better fitness than the CoMSIA model 2 (r2 cv.(q2)=0.970, r2 ncv.=0.886). The contour maps showed that, the inhibitory activities of the analogues against breast cancer cells were expected to increase when hydrophilic and steric favor groups with less than five carbon atoms were substituted at the R1 position. However, it was predicted that the negative charge R2 favor group and hydrophobic favor, along with the positive charge favor and steric R3 disfavor group will achieve the inhibitory activity. The inhibitory activity (IC50=0.0018 ppm) against breast cancer cells of the newly designed molecule (P1) with optimized CoMFA model 2 was 20-fold higher than that of the commercialized drug, Docetaxel (IC50=0.04 ppm). Three-dimensional quantitative structure-activity relationships (3D-QSARs) on the inhibitory activity of [(2-phenylindol-3-yl)methylene]propanedinitrile analogues (1~19) against human breast cancer cells (MDA-MB 231) were studied using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The optimized CoMFA model 2 (r2 cv.(q2)=0.581, r2 ncv.=0.970) model predictability was lower than that of CoMSIA model 2, but showed better fitness than the CoMSIA model 2 (r2 cv.(q2)=0.970, r2 ncv.=0.886). The contour maps showed that, the inhibitory activities of the analogues against breast cancer cells were expected to increase when hydrophilic and steric favor groups with less than five carbon atoms were substituted at the R1 position. However, it was predicted that the negative charge R2 favor group and hydrophobic favor, along with the positive charge favor and steric R3 disfavor group will achieve the inhibitory activity. The inhibitory activity (IC50=0.0018 ppm) against breast cancer cells of the newly designed molecule (P1) with optimized CoMFA model 2 was 20-fold higher than that of the commercialized drug, Docetaxel (IC50=0.04 ppm).

QM and Pharmacophore based 3D-QSAR of MK886 Analogues against mPGES-1

Pasha, F.A.,Muddassar, M.,Jung, Hwan-Won,Yang, Beom-Seok,Lee, Cheol-Ju,Oh, Jung-Soo,Cho, Seung-Joo,Cho, Hoon Korean Chemical Society 2008 Bulletin of the Korean Chemical Society Vol.29 No.3

Microsomal prostaglandin E2 synthase (mPGES-1) is a potent target for pain and inflammation. Various QSAR (quantitative structure activity relationship) analyses used to understand the factors affecting inhibitory potency for a series of MK886 analogues. We derived four QSAR models utilizing various quantum mechanical (QM) descriptors. These QM models indicate that steric, electrostatic and hydrophobic interaction can be important factors. Common pharmacophore hypotheses (CPHs) also have studied. The QSAR model derived by best-fitted CPHs considering hydrophobic, negative group and ring effect gave a reasonable result (q2 = 0.77, r2 = 0.97 and Rtestset = 0.90). The pharmacophore-derived molecular alignment subsequently used for 3D-QSAR. The CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) techniques employed on same series of mPGES-1 inhibitors which gives a statistically reasonable result (CoMFA; q2 = 0.90, r2 = 0.99. CoMSIA; q2 = 0.93, r2 = 1.00). All modeling results (QM-based QSAR, pharmacophore modeling and 3D-QSAR) imply steric, electrostatic and hydrophobic contribution to the inhibitory activity. CoMFA and CoMSIA models suggest the introduction of bulky group around ring B may enhance the inhibitory activity.

Toward Proper 3D-QSAR Datasets for Parameter Evaluation

조승주 조선대학교 기초과학연구원 2011 조선자연과학논문집 Vol.4 No.3

3D-QSAR techniques including CoMFA have been used a lot for more than two decades now. For now, the perspective of 3D-QSAR has been changed. The realization of gorge activity cliffs and higher chance correlation with many independent variables (IVs) has changed the requirements. Some suggested the benchmarking datasets for 3D-QSAR. However, were they still useful for right reasons? Here, we propose the requirement of any general purpose 3D-QSAR benchmarking datasets for lead optimization, especially for feasibility test of any IVs. Specifically, we summarize the conceptual requirements for an ideal settings for 3D-QSAR especially CoMFA.

Toward Proper 3D-QSAR Datasets for Parameter Evaluation

Cho, Seung Joo The Basic Science Institute Chosun University 2011 조선자연과학논문집 Vol.4 No.3

3D-QSAR techniques including CoMFA have been used a lot for more than two decades now. For now, the perspective of 3D-QSAR has been changed. The realization of gorge activity cliffs and higher chance correlation with many independent variables (IVs) has changed the requirements. Some suggested the benchmarking datasets for 3D-QSAR. However, were they still useful for right reasons? Here, we propose the requirement of any general purpose 3D-QSAR benchmarking datasets for lead optimization, especially for feasibility test of any IVs. Specifically, we summarize the conceptual requirements for an ideal settings for 3D-QSAR especially CoMFA.

A Review of 3D-QSAR in Drug Design

Thirumurthy Madhavan 조선대학교 기초과학연구원 2012 조선자연과학논문집 Vol.5 No.1

2-[(2,6-Dioxocyclohexyl)methyl]cyclohexane-1,3-dione 유도체의 Tyrosinase 저해활성에 관한 2D-QSAR 분석

김상진 ( Sang-jin Kim ),성낙도 ( Nack-do Sung ) 대한화장품학회 2014 대한화장품학회지 Vol.40 No.4

기질 분자로서 2-[(2,6-dioxocyclohexyl)methyl]cyclohexane-1,3-dione 유도체(1-23)들의 분자 내 치환기(R1 및 R2)가 변화함에 따른 tyrosinase 수용체의 저해활성에 관한 2D-QSAR 모델로부터 다음과 같은 결론을 얻었다. 유도된 최적의 2D-QSAR 모델은 Obs.pI50 = -0.295 (± 0.031)TDM -0.120 (±0.014)DMZ + 0.135 (± 0.050)DMX. R2 + 6.382 (± 0.17)이었으며, 예측성(q2 = 0.843)보다는 상관성(r2 = 0.905)이 큰 모델이었다. Tyrosinase 저해활성은 TDM > DMX.R2 ≥ DMZ 순으로 영향을 미치었으며, 기질분자의 소수성(ClogP > 0)이 크고, R1-치환기의 입체적 크기가 클수록 더욱 증가하는 경향을 나타내었다. 모델을 분석한 결과, 분자 내 R2-치환기 상 X-축 성분의 쌍극자능률(DMX.R2)이 클수록, 그리고 분자 전체의 쌍극자능률(TDM; Total Dipole Moment)과 Z-성분의 쌍극자능률(DMZ; Dipole Moment of Z-Component)이 작을수록 기질분자의 tyrosinase 저해활성이 높아짐을 암시하였다. 따라서 tyrosinase 저해 활성은 기질분자 및 R2-치환기의 전자 친화력에 기인한 것으로 예상되었다. 그러므로 저해활성을 증가시키려면 분자 내 극성 그룹을 소수성에 기여하는 비극성 작용기로 대체함이 바람직할 것으로 예측되었다. The following conclusion was made from the 2D-QSAR model for the tyrosinase inhibitory activity according to the variation of the substituents R1 and R2 in analogues of compound 2-[(2,6-dioxocyclohexyl)methyl]cyclohexane- 1,3-dione (1-23). The best optimized 2D-QSAR model was Obs.pI50 = -0.295 (± 0.031)TDM -0.120 (± 0.014 )DMZ + 0.135 (± 0.050)DMX. R2 + 6.382 (± 0.17), and the correlation (r2 = 0.905) of which was greater than its predictability (q2 = 0.843). The magnitude of the effect of tyrosinase inhibitory activities was in order of TDM > DMX.R2 ≥ DMZ, and it tended to increase as the hydrophobicity of substrate molecule (ClogP > 0) as well as the steric favor of substituent R1 increased. The analysis of the model implies that inhibitory activity of substrate molecule will increase as DMX.R2 (Dipole moment X component of R2-substituent) increases, while TDM (Total Dipole Moment) and DMZ (Dipole Moment of Z-Component) decrease. As such, it is deemed feasible to conclude, that in order to increase the inhibitory effect, it would be rather desirable to replace the polar groups within the molecules with non-polar functional groups.

Design of Novel JNK3 Inhibitors Based on 3D-QSAR In Silico Model

Madhavan, Thirumurthy The Basic Science Institute Chosun University 2012 조선자연과학논문집 Vol.5 No.1

c-Jun N-terminal kinase-3 (JNK-3) has been identified as a promising target for neuronal apoptosis and has the effective therapeutic for neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and other CNS disorders. Herein, we report the essential structural and chemical parameters for JNK-3 inhibitors utilizing comparative molecular field similarity indices analysis (CoMSIA) using the derivatives of 3,5-disubstituted quinolines. The best predictions were obtained CoMSIA model (q2=0.834, r2=0.987) and the statistical parameters from the generated 3D-QSAR models were indicated that the data are well fitted and have high predictive ability. The resulting contour map from 3D-QSAR models might be helpful to design novel and more potent JNK3 derivatives.