Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for the rising amounts of data, but the gap between what these neural networks learn and what human beings can comprehend is growing. Moreover, this gap may induce distrust and restrict deep learning applications in practice. Here, we introduce a new graph neural network architecture called Attentive FP for molecular representation that uses a graph attention mechanism to learn from relevant drug discovery data sets. We demonstrate that Attentive FP achieves state-of-the-art predictive performances on a variety of data sets and that what it learns is interpretable. The feature visualization for Attentive FP suggests that it automatically learns nonlocal intramolecular interactions from specified tasks, which can help us gain chemical insights directly from data beyond human perception.

Motivation Identifying compound-protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional (3D) structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI. However, sequence-based CPI models may face some specific pitfalls, including using inappropriate datasets, hidden ligand bias, and splitting datasets inappropriately, resulting in overestimation of their prediction performance. Results To address these issues, we here constructed new datasets specific for CPI prediction, proposed a novel transformer neural network named TransformerCPI, and introduced a more rigorous label reversal experiment to test whether a model learns true interaction features. TransformerCPI achieved much improved performance on the new experiments, and it can be deconvolved to highlight important interacting regions of protein sequences and compound atoms, which may contribute chemical biology studies with useful guidance for further ligand structural optimization. Supplementary information Supplementary data are available at Bioinformatics online. Availability and implementation https://github.com/lifanchen-simm/transformerCPI

Abrogating tumor angiogenesis by inhibiting vascular endothelial growth factor receptor‐2 (VEGFR2) has been established as a therapeutic strategy for treating cancer. However, because of their low selectivity, most small molecule inhibitors of VEGFR2 tyrosine kinase show unexpected adverse effects and limited anticancer efficacy. In the present study, we detailed the pharmacological properties of anlotinib, a highly potent and selective VEGFR2 inhibitor, in preclinical models. Anlotinib occupied the ATP‐binding pocket of VEGFR2 tyrosine kinase and showed high selectivity and inhibitory potency (IC 50 <1 nmol/L) for VEGFR2 relative to other tyrosine kinases. Concordant with this activity, anlotinib inhibited VEGF‐induced signaling and cell proliferation in HUVEC with picomolar IC 50 values. However, micromolar concentrations of anlotinib were required to inhibit tumor cell proliferation directly in vitro. Anlotinib significantly inhibited HUVEC migration and tube formation; it also inhibited microvessel growth from explants of rat aorta in vitro and decreased vascular density in tumor tissue in vivo. Compared with the well‐known tyrosine kinase inhibitor sunitinib, once‐daily oral dose of anlotinib showed broader and stronger in vivo antitumor efficacy and, in some models, caused tumor regression in nude mice. Collectively, these results indicate that anlotinib is a well‐tolerated, orally active VEGFR2 inhibitor that targets angiogenesis in tumor growth, and support ongoing clinical evaluation of anlotinib for a variety of malignancies.

Abstract. In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

The low immunogenicity, insufficient infiltration of T lymphocytes, and dismal response to immune checkpoint blockade therapy pose major difficulties in immunotherapy of pancreatic cancer. Photoimmunotherapy by photodynamic therapy (PDT) can induce an antitumor immune response by triggering immunogenic cell death in the tumor cells. Notwithstanding, PDT‐driven oxygen consumption and microvascular damage can further aggravate hypoxia to exaggerates glycolysis, leading to lactate accumulation and immunosuppressive tumor microenvironment. Herein, a supramolecular prodrug nanoplatform codelivering a photosensitizer and a prodrug of bromodomain‐containing protein 4 inhibitor (BRD4i) JQ1 for combinatory photoimmunotherapy of pancreatic cancer are demonstrated. The nanoparticles are fabricated by host–guest complexation between cyclodextrin‐grafted hyaluronic acid (HA‐CD) and adamantine‐conjugated heterodimers of pyropheophorbide a (PPa) and JQ1, respectively. HA can achieve active tumor targeting by recognizing highly expressed CD44 on the surface of pancreatic tumors. PPa‐mediated PDT can enhance the immunogenicity of the tumor cells and promote intratumoral infiltration of the cytotoxic T lymphocytes. Meanwhile, JQ1 combats PDT‐mediated immune evasion through inhibiting expression of c‐Myc and PD‐L1, which are key regulators of tumor glycolysis and immune evasion. Collectively, this study presents a novel strategy to enhance photoimmunotherapy of the pancreatic cancer by provoking T cells activation and overcoming adaptive immune resistance.

The C-terminal domain of the bacterial transcription antiterminator RfaH undergoes a dramatic all-α-helix to all-β-barrel transition when released from its N-terminal domain. These two distinct folding patterns correspond to different functions: the all-α state acts as an essential regulator of transcription to ensure RNA polymerase binding, whereas the all-β state operates as an activator of translation by interacting with the ribosomal protein S10 and recruits ribosomal mRNA. Accordingly, this drastic conformational change enables RfaH to physically couple the transcription and translation processes in gene expression. To understand the mechanism behind this extraordinary functionally relevant structural transition, we constructed Markov state models using an adaptive seeding method. The constructed models highlight several parallel folding pathways with heterogeneous molecular mechanisms, which reveal the folding kinetics and atomic details of the conformational transition.

p300 and CREB-binding protein (CBP) are ubiquitously expressed pleiotropic lysine acetyltransferases and play a key role as transcriptional co-activators that are essential for a multitude of cellular processes. Despite great importance, there is a lack of highly selective, potent, druglike p300/CBP inhibitors. Through the artificial-intelligence-assisted drug discovery pipeline and further optimization, we reported the discovery of novel, highly selective, potent small-molecule inhibitors of p300/CBP histone acetyltransferases (HAT) with desired druglike properties, exemplified by B026. Our data demonstrated that B026, with half maximal inhibitory concentration (IC50) values of 1.8 nM to p300 and 9.5 nM to CBP enzyme inhibitory activity, is the most potent, selective p300/CBP HAT inhibitor. Moreover, B026 achieves significant and dose-dependent tumor growth inhibition in an animal model of human cancer, suggesting that B026 is a highly promising p300/CBP HAT inhibitor and warrants extensive preclinical investigation as a potential clinical development candidate.

Polycomb Repressive Complex 2 (PRC2) modulates the chromatin structure and transcriptional repression by trimethylation lysine 27 of histone H3 (H3K27me3), a process that necessitates the protein-protein interaction (PPI) between the catalytic subunit EZH2 and EED. Deregulated PRC2 is intimately involved in tumorigenesis and progression, making it an invaluable target for epigenetic cancer therapy. However, until now, there have been no reported small molecule compounds targeting the EZH2-EED interactions. In the present study, we identified astemizole, an FDA-approved drug, as a small molecule inhibitor of the EZH2-EED interaction of PRC2. The disruption of the EZH2-EED interaction by astemizole destabilizes the PRC2 complex and inhibits its methyltransferase activity in cancer cells. Multiple lines of evidence have demonstrated that astemizole arrests the proliferation of PRC2-driven lymphomas primarily by disabling the PRC2 complex. Our findings demonstrate the chemical tractability of the difficult PPI target by a small molecule compound, highlighting the therapeutic promise for PRC2-driven human cancers via targeted destruction of the EZH2-EED complex.