Deep learning in drug design and discovery / 药学学报

Among various technologies used in drug design and discovery, deep learning is still in its infancy. Recently, deep learning approaches have been rapidly developed and applied to address various problems in drug discovery, including generation of virtual compound library, prediction of compound activity, metabolism and toxicity, and prediction of organic synthesis routes. Compared with the traditional machine learning methods, the prediction power of deep learning did not show significant improvement. However, proactively learning and automatically feature extraction bring advantages for deep learning approaches. Compared to first principle-based computational chemistry methods, deep learning can not be generalized because it depends on large-scale and high-quality annotated data sets. But its molecular representation with single-atom atomic environment vectors could be useful for computational chemists. As an emerging technology, deep learning, especially the unsupervised learning method that does not rely on large datasets with labels, is gradually improving. It is expected that someday deep learning method will become practical for drug discovery.

Structure-based identification of drug-like inhibitors of p300 histone acetyltransferase / 药学学报

A growing body of evidence suggests that p300 histone acetyltransferase plays important roles in cancer cell differentiation and proliferation. Here, we employed structure-based hierarchical virtual screening method to identify novel lead compounds of p300 histone acetyltransferase. From a screening library containing approximate 100 000 diverse druglike compounds, 33 compounds were chosen for experimental testing and one compound, 4-acetyl-2-methyl-N-morpholino-3,4-dihydro-2H-benzo[b][1, 4]thiazine-7-sulfonamide (17), showed as micromolar inhibitor. Based on its predicted binding pose, we investigated its binding characteristics by designing two series of structural modifications. The obtained structure-activity relationship results are consistent with the predicted binding model. We expect that the identified novel p300 histone acetyltransferase inhibitors will serve as starting points for further development of more potent and specific histone acetyltransferase inhibitors.

Computational chemistry in structure-based drug design / 药学学报

Today, the understanding of the sequence and structure of biologically relevant targets is growing rapidly and researchers from many disciplines, physics and computational science in particular, are making significant contributions to modern biology and drug discovery. However, it remains challenging to rationally design small molecular ligands with desired biological characteristics based on the structural information of the drug targets, which demands more accurate calculation of ligand binding free-energy. With the rapid advances in computer power and extensive efforts in algorithm development, physics-based computational chemistry approaches have played more important roles in structure-based drug design. Here we reviewed the newly developed computational chemistry methods in structure-based drug design as well as the elegant applications, including binding-site druggability assessment, large scale virtual screening of chemical database, and lead compound optimization. Importantly, here we address the current bottlenecks and propose practical solutions.

Structure-based identification of drug-like inhibitors of p300 histone acetyltransferase / 药学学报

A growing body of evidence suggests that p300 histone acetyltransferase plays important roles in cancer cell differentiation and proliferation. Here, we employed structure-based hierarchical virtual screening method to identify novel lead compounds of p300 histone acetyltransferase. From a screening library containing approximate 100 000 diverse druglike compounds, 33 compounds were chosen for experimental testing and one compound, 4-acetyl-2-methyl-N-morpholino-3,4-dihydro-2H-benzo[b][1, 4]thiazine-7-sulfonamide (17), showed as micromolar inhibitor. Based on its predicted binding pose, we investigated its binding characteristics by designing two series of structural modifications. The obtained structure-activity relationship results are consistent with the predicted binding model. We expect that the identified novel p300 histone acetyltransferase inhibitors will serve as starting points for further development of more potent and specific histone acetyltransferase inhibitors.

Computational chemistry in structure-based drug design / 药学学报

Today, the understanding of the sequence and structure of biologically relevant targets is growing rapidly and researchers from many disciplines, physics and computational science in particular, are making significant contributions to modern biology and drug discovery. However, it remains challenging to rationally design small molecular ligands with desired biological characteristics based on the structural information of the drug targets, which demands more accurate calculation of ligand binding free-energy. With the rapid advances in computer power and extensive efforts in algorithm development, physics-based computational chemistry approaches have played more important roles in structure-based drug design. Here we reviewed the newly developed computational chemistry methods in structure-based drug design as well as the elegant applications, including binding-site druggability assessment, large scale virtual screening of chemical database, and lead compound optimization. Importantly, here we address the current bottlenecks and propose practical solutions.

Identification of novel inhibitors of the streptogramin group A acetyltransferase via virtual screening / 药学学报

Virginiamycin acetyltransferase D (VatD) plays a vital rule in streptogramins resistance by chemically inactivating streptogramin A. Therefore, it is desirable to discover novel small molecular weight inhibitors of VatD via state-of-the-art virtual screening techniques. This "cocktail" strategy by combining VatD inhibitor with streptogramins may provide new therapeutic opportunity for resistant bacteria infections. Structure-based virtual screening method (molecular docking) was applied to rank and score a chemical database containing 300 000 commercially available compounds against the VatD substrate binding site. Twenty six out of the 200 top scored compounds from the docking calculation were selected and submitted to the VatD enzymatic inhibition assay. The plasmid pRSET B/vatD was constructed and transformed into E.coli (trxB) host cells for over-expression, and VatD enzyme was purified and validated by showing acetyltransferase activity to Virginiamycin M1. Three out of these 26 tested compounds showed enzymatic inhibition on VatD with IC50 168.6, 91.0 and 55.2 μmol·L-1, separately. Other compounds could not be dissolved in the system and/or had little effect on the enzyme (IC50>200 μmol·L-1). To our knowledge, it is first time that small molecular weight organic compounds were identified as VatD inhibitors. It is expected that the VatD inhibitors identified at present study could serve as lead compounds for the further development of the novel therapeutic agents to overcome streptogramins resistance.

Design and activity determination of small molecular inhibitors of integrin alphavbeta3 / 中国医学科学院学报

<p><b>OBJECTIVE</b>To explore the design and activity determination of small molecular inhibitors of integrin alphavbeta3 through structure-based virtual screening.</p><p><b>METHODS</b>Based on the crystal structure of integrin ctv33 extracellular segment in complex with an ARG-GLY-ASP ligand, docking procedure against the receptor binding domain was performed on 3D database. Integrin alphavbeta3-mediated cell adhesion assay was performed to assess the adhesion-inhibiting ability of the candidate compounds. Cell migration assay and capillary-structure-like formation inhibition assay were used to estimate the effects of the compounds on integrin alphavbeta3. Analysis of molecular graphics was carried out to deduce a probable binding model of compound with integrin alphavbeta3.</p><p><b>RESULTS</b>From the top 1000 compounds with the best DOCK energy score, 50 compounds were selected for biological assay based on chemical and drug-like diversity. Seven of 50 compounds showed notable inhibition activity on cell adhesion, and two with half-maximum inhibition concentration (IC50) values less than 100 mol/L. The compound with best activity (1-37) showed high inhibitory activity in cell migration assay and capillary-structure-like formation inhibition assay. Molecular graphics analysis indicated that metal ion-dependent adhesion site (MIDAS) might be involved in the compound 1-37-mediated inhibition of ligand binding with integrin alphavbeta3.</p><p><b>CONCLUSIONS</b>Through virtual screening combined with biological assay, a promising lead compound was discovered to inhibit integrin alphavbeta3, which embodies the rational drug design with computation aid and brings a new thought and approach to find novel inhibitors of integrin.</p>

Identification of novel inhibitors of the streptogramin group A acetyltransferase via virtual screening / 药学学报

Virginiamycin acetyltransferase D (VatD) plays a vital rule in streptogramins resistance by chemically inactivating streptogramin A. Therefore, it is desirable to discover novel small molecular weight inhibitors of VatD via state-of-the-art virtual screening techniques. This "cocktail" strategy by combining VatD inhibitor with streptogramins may provide new therapeutic opportunity for resistant bacteria infections. Structure-based virtual screening method (molecular docking) was applied to rank and score a chemical database containing 300 000 commercially available compounds against the VatD substrate binding site. Twenty six out of the 200 top scored compounds from the docking calculation were selected and submitted to the VatD enzymatic inhibition assay. The plasmid pRSET B/vatD was constructed and transformed into E. coli (trxB) host cells for over-expression, and VatD enzyme was purified and validated by showing acetyltransferase activity to Virginiamycin M1. Three out of these 26 tested compounds showed enzymatic inhibition on VatD with IC50 168.6, 91.0 and 55.2 micromol x L(-1), separately. Other compounds could not be dissolved in the system and/or had little effect on the enzyme (IC50 > 200 micromol x L(-1)). To our knowledge, it is first time that small molecular weight organic compounds were identified as VatD inhibitors. It is expected that the VatD inhibitors identified at present study could serve as lead compounds for the further development of the novel therapeutic agents to overcome streptogramins resistance.

Molecular design and biological activity of BCR-ABL tyrosine kinase inhibitors / 中国医学科学院学报

<p><b>OBJECTIVE</b>To discover BCR-ABL tyrosine kinase inhibitors through structure based virtual screening.</p><p><b>METHODS</b>Docking screening against the distinctive inactive conformation of the catalytic domain of BCR-ABL tyrosine kinase was performed on 3D database. The MTT assay was performed to assess the viability of the tumor cells treated with selected compounds. The amount and kinase activity of BCR-ABL protein were detected in the presence of compounds by Western blot analysis and immunoprecipitation.</p><p><b>RESULTS</b>From the top 1,000 compounds with the best DOCK energy score, 15 compounds were selected for biological assay. Eight out of 15 compounds showed notable inhibitory activity against Ph+ human K562 cells with IC50 values ranging from 10 to 200 micromol/L. In cell-based assays of ABL tyrosine phosphorylation, the ability of two kinds of novel, structurally diverse, lead compounds to inhibit ABL kinase activity was observed. However, no significant differences in the amount of BCR-ABL protein were noted on the ABL immunoblot in the presence of these lead compounds.</p><p><b>CONCLUSIONS</b>Two promising lead compounds were discovered to inhibit BCR-ABL tyrosine kinase activity. Virtual screening technique has been proven to narrow down the size of screening compound libraries to the most prospective drug candidates with high success rates.</p>