Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.

De novo drug design is a stationary way to build novel ligands in the confined pocket of receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation is a dynamical way to study the interaction mechanism between the ligands and receptors based on the molecular force field. De novo drug design and MD simulation are effective tools for novel drug discovery. With the development of technology, deep learning methods, and interpretable machine learning (IML) have emerged in the research area of drug design. Deep learning methods and IML can be used further to improve the efficiency and accuracy of de novo drug design and MD simulations. The application summary of deep learning methods for de novo drug design, MD simulations, and IML can further promote the technical development of drug discovery. In this article, two major workflow methods and the related components of classical algorithm and deep learning are described for de novo drug design from a new perspective. The application progress of deep learning is also summarized for MD simulations. Furthermore, IML is introduced for the deep learning model interpretability of de novo drug design and MD simulations. Our paper deals with an interesting topic about deep learning applications of de novo drug design and MD simulations for the scientific community.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

Epidermal growth factor receptor (EGFR) is a clinical therapeutic target to treat a subset of non–small cell lung cancer (NSCLC) harboring EGFR mutants. However, some patients with a similar kind of EGFR mutation show intrinsic resistance to tyrosine kinase inhibitors (TKI). It indicates that other key molecules are involved in the survival of these cancer cells. We showed here that 2-[(aminocarbonyl)amino]-5 -(4-fluorophenyl)-3- thiophenecarboxamide (TPCA-1), a previously reported inhibitor of IκB kinases (IKK), blocked STAT3 recruitment to upstream kinases by docking into SH2 domain of STAT3 and attenuated STAT3 activity induced by cytokines and cytoplasmic tyrosine kinases. TPCA-1 is an effective inhibitor of STAT3 phosphorylation, DNA binding, and transactivation in vivo. It selectively repressed proliferation of NSCLC cells with constitutive STAT3 activation. In addition, using pharmacologic and genetic approaches, we found that both NF-κB and STAT3 could regulate the transcripts of interleukin (IL)-6 and COX-2 in NSCLC harboring EGFR mutations. Moreover, gefitinib treatment only did not efficiently suppress NF-κB and STAT3 activity. In contrast, we found that treatment with TKIs increased phosho-STAT3 level in target cells. Inhibiting EGFR, STAT3, and NF-κB by combination of TKIs with TPCA-1 showed increased sensitivity and enhanced apoptosis induced by gefitinib. Collectively, in this work, we identified TPCA-1 as a direct dual inhibitor for both IKKs and STAT3, whereas treatment targeting EGFR only could not sufficiently repress NF-κB and STAT3 pathways for lung cancers harboring mutant EGFR. Therefore, synergistic treatment of TPCA-1 with TKIs has potential to be a more effective strategy for cancers. Mol Cancer Ther; 13(3); 617–29. ©2014 AACR.

Most proinflammatory actions of C-reactive protein (CRP) are only expressed following dissociation of its native pentameric assembly into monomeric form (mCRP). However, little is known about what underlies the greatly enhanced activities of mCRP. Here we show that a single sequence motif, i.e. cholesterol binding sequence (CBS; a.a. 35–47), is responsible for mediating the interactions of mCRP with diverse ligands. The binding of mCRP to lipoprotein component ApoB, to complement component C1q, to extracellular matrix components fibronectin and collagen, to blood coagulation component fibrinogen, and to membrane lipid component cholesterol, are all found to be markedly inhibited by the synthetic CBS peptide but not by other CRP sequences tested. Likewise, mutating CBS in mCRP also greatly impairs these interactions. Functional experiments further reveal that CBS peptide significantly reduces the effects of mCRP on activation of endothelial cells in vitro and on acute induction of IL-6 in mice. The potency and specificity of CBS are critically determined by the N-terminal residues Cys-36, Leu-37, and His-38; while the versatility of CBS appears to originate from its intrinsically disordered conformation polymorphism. Together, these data unexpectedly identify CBS as the major recognition site of mCRP and suggest that this motif may be exploited to tune the proinflammatory actions of mCRP.