Precise prediction of bioactive conformations represents a central challenge in drug discovery. Traditional computational methods often rely on the assumption that the global minimum energy state corresponds to the bioactive conformation─a hypothesis with inherent limitations. Data-driven artificial intelligence (AI) models have revolutionized research methodologies in this domain. By learning the implicit probability distributions of conformations, AI demonstrates the potential to achieve high-precision predictions of bioactive structures. This review systematically surveys technical advancements ranging from geometric regression to generative and conditional generative models, analyzing their effectiveness in benchmarks and de novo design experiments. We deeply explore critical challenges, including data sparsity, sampling efficiency, generation capability, and interpretability. Finally, we provide an outlook on bridging the gap from "structural prediction" to "functional design" through the construction of dynamic data sets, the development of efficient algorithms, and the implementation of human-in-the-loop collaborative decision-making systems.

Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale because covalent docking requires determination of the covalent reaction type of the compound. Here, we propose to use deep learning of a lateral interactions spiking neural network to construct a covalent lead compound screening model to quickly screen covalent lead compounds. We used the 3CL protease (3CL Pro) of SARS-CoV-2 as the screen target and constructed two classification models based on LISNN to predict the covalent binding and inhibitory activity of compounds. The two classification models were trained on the covalent complex data set targeting cysteine (Cys) and the compound inhibitory activity data set targeting 3CL Pro, respected, with good prediction accuracy (ACC > 0.9). We then screened the screening compound library with 6 covalent binding screening models and 12 inhibitory activity screening models. We tested the inhibitory activity of the 32 compounds, and the best compound inhibited SARS-CoV-2 3CL Pro with an IC50 value of 369.5 nM. Further assay implied that dithiothreitol can affect the inhibitory activity of the compound to 3CL Pro, indicating that the compound may covalently bind 3CL Pro. The selectivity test showed that the compound had good target selectivity to 3CL Pro over cathepsin L. These correlation assays can prove the rationality of the covalent lead compound screening model. Finally, covalent docking was performed to demonstrate the binding conformation of the compound with 3CL Pro. The source code can be obtained from the GitHub repository (https://github.com/guzh970630/Screen_Covalent_Compound_by_LISNN).

Background: The epidemic caused by SARS-CoV-2 swept the world in 2019. The 3C-like protease (3CLpro) of SARS-CoV-2 plays a key role in viral replication, and its inhibition could inhibit viral replication. Materials & methods: The virtual screen based on receptor–ligand pharmacophore models and molecular docking were conducted to obtain the novel scaffolds of the 3CLpro. The molecular dynamics simulation was also carried out. All compounds were synthesized and evaluated in biochemical assays. Results: The compound C2 could inhibit 3CLpro with a 72% inhibitory rate at 10 μM. The covalent docking showed that C2 could form a covalent bond with the Cys145 in 3CLpro. Conclusion: C2 could be a potent lead compound of 3CLpro inhibitors against SARS-CoV-2.

IntroductionThe TGF-beta signaling pathway is a complex network that plays a crucial role in regulating essential biological functions and is implicated in the onset and progression of multiple diseases. This review highlights the recent advancements in developing inhibitors targeting the TGF-beta signaling pathway and their potential therapeutic applications in various diseases.Area coveredThe review discusses patents on active molecules related to the TGF-beta signaling pathway, focusing on three strategies: TGF-beta activity inhibition, blocking TGF-beta receptor binding, and disruption of the signaling pathway using small molecule inhibitors. Combination therapies and the development of fusion proteins targeting multiple pathways are also explored. The literature search was conducted using the Cortellis Drug Discovery Intelligence database, covering patents from 2021 onwards.Expert opinionThe development of drugs targeting the TGF-beta signaling pathway has made significant progress in recent years. However, addressing challenges such as specificity, systemic toxicity, and patient selection is crucial for their successful clinical application. Targeting the TGF-beta signaling pathway holds promise as a promising approach for the treatment of various diseases.

良好的先导化合物对于药物研发具有深远影响,可以提高药物上市的成功率.利用传统方法发现先导化合物存在成本高且耗时的问题,而人工智能(artificial intelligence,AI)可以高效发现良好的先导化合物.本文系统地总结了通过人工智能的筛选模型与生成模型获得先导化合物的研究进展,按照输入信息的类型归纳整理不同的模型,重点介绍了利用筛选模型实现药物重定位和利用生成模型实现多目标药物设计,探讨了人工智能在先导化合物研究领域的发展前景,为人工智能在先导化合物方面的应用提供新的研究思路.

Identifying compound–protein interaction plays a vital role in drug discovery. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are playing increasingly important roles in compound-protein interaction (CPI) prediction. However, ML relies on learning from large sample data. And the CPI for specific target often has a small amount of data available. To overcome the dilemma, we propose a virtual screening model, in which word2vec is used as an embedding tool to generate low-dimensional vectors of SMILES of compounds and amino acid sequences of proteins, and the modified multi-grained cascade forest based gcForest is used as the classifier. This proposed method is capable of constructing a model from raw data, adjusting model complexity according to the scale of datasets, especially for small scale datasets, and is robust with few hyper-parameters and without over-fitting. We found that the proposed model is superior to other CPI prediction models and performs well on the constructed challenging dataset. We finally predicted 2 new inhibitors for clusters of differentiation 47(CD47) which has few known inhibitors. The IC50s of enzyme activities of these 2 new small molecular inhibitors targeting CD47-SIRPα interaction are 3.57 and 4.79 μM respectively. These results fully demonstrate the competence of this concise but efficient tool for CPI prediction.

Introduction Fibrosis is a disease that damages organs and even causes death. Because of the complicated pathogenesis, the development of drugs for fibrosis is challenging. In the lysophosphatidic acid receptor type 1 (LPA1) signaling pathway, LPA1 and its downstream Rho-associated coiled-coil forming protein kinase (ROCK) are related to the process of fibrosis. Targeting LPA1 signaling pathway is a potential strategy for the treatment of fibrosis. Area covered This review describes the process of fibrosis mediated by the LPA1 signaling pathway and then summarizes LPA1 antagonist patents reported since 2010 and ROCK inhibitor patents since 2017 according to their scaffolds based on the Cortellis Drug Discovery Intelligence database. Information on LPA1 antagonists entering clinical trials is integrated. Expert opinion Over the past decade, a large number of antagonists targeting the LPA1 signaling pathway have been patented for fibrosis therapy. A limited number of compounds have entered clinical trials. Different companies and research groups have used different scaffolds when designing compounds for fibrosis therapy. Therefore, LPA1 and ROCK are competitive targets for the development of new therapies for fibrosis to provide a potential treatment method for fibrosis in the future.

Cluster of differentiation 47 (CD47)/signal regulatory protein alpha (SIRPα) is a negative innate immune checkpoint signaling pathway that restrains immunosurveillance and immune clearance, and thus has aroused wide interest in cancer immunotherapy. Blockade of the CD47/SIRPα signaling pathway shows remarkable antitumor effects in clinical trials. Currently, all inhibitors targeting CD47/SIRPα in clinical trials are biomacromolecules. The poor permeability and undesirable oral bioavailability of biomacromolecules have caused researchers to develop small-molecule CD47/SIRPα pathway inhibitors. This review will summarize the recent advances in CD47/SIRPα interactions, including crystal structures, peptides and small molecule inhibitors. In particular, we have employed computer-aided drug discovery (CADD) approaches to analyze all the published crystal structures and docking results of small molecule inhibitors of CD47/SIRPα, providing insight into the key interaction information to facilitate future development of small molecule CD47/SIRPα inhibitors.

Virtual screening is an important means for lead compound discovery. The scoring function is the key to selecting hit compounds. Many scoring functions are currently available; however, there are no all-purpose scoring functions because different scoring functions tend to have conflicting results. Recently, neural networks, especially convolutional neural networks, have constantly been penetrating drug design and most CNN-based virtual screening methods are superior to traditional docking methods, such as Dock and AutoDock. CNN-based virtual screening is expected to improve the previous model of overreliance on computational chemical screening. Utilizing the powerful learning ability of neural networks provides us with a new method for evaluating compounds. We review the latest progress of CNN-based virtual screening and propose prospects.

Fibrosis is a serious disease that occurs in many organs, such as kidney, liver and lung. The deterioration of these organs ultimately leads to death. Due to the complex mechanisms of fibrosis, research and development of antifibrotic drugs is difficult. One solution is to focus on core pathways, one of which is the TGF-β signaling pathway. In virtually every type of fibrosis, TGF-β signaling is recognized as a critical pathway.This review discusses patents on active molecules related to the TGF-β signaling. Molecules targeting components related to the activation of TGF-β are introduced. Several strategies preventing signal propagation from active TGF-β to downstream targets are also introduced, including TGF-β antibodies, TGF-β ligand traps, and inhibitors of TGF-β receptor kinases. Finally, molecules affecting downstream targets in both canonical and noncanonical TGF-β signaling pathways are described.Since the approval of pirfenidone, targeting TGF-β signaling has been anticipated as an effective therapy for fibrosis. The potential of this therapy has been further supported by emerging patents on the TGF-β signaling. This pathway can be entirely inhibited, from the activation of TGF-β to downstream signaling. Inhibiting TGF-β signaling is expected to provide more effective treatments for fibrosis.

Virtual screening is an important means for lead compound discovery. The scoring function is the key to selecting hit compounds. Many scoring functions are currently available; however, there are no all-purpose scoring functions because different scoring functions tend to have conflicting results. Recently, neural networks, especially convolutional neural networks, have constantly been penetrating drug design and most CNN-based virtual screening methods are superior to traditional docking methods, such as Dock and AutoDock. CNNbased virtual screening is expected to improve the previous model of overreliance on computational chemical screening. Utilizing the powerful learning ability of neural networks provides us with a new method for evaluating compounds. We review the latest progress of CNN-based virtual screening and propose prospects.

ETHNOPHARMACOLOGICAL RELEVANCE:Huiyang Shengji formula (HSF) is a compound Chinese herbal medicine prescription, and has long been used for treating chronic non-healing wounds.AIM OF THE STUDY:The purpose of this study was to provide new insight into molecular mechanisms of healing effects of the HSF treatments.MATERIALS AND METHODS:We established a rat diabetic skin ulcer (DSU) model, and assessed healing effects of four HSF treatments on DSUs by calculating wound healing rates and immunohistochemical detection of the expressions of angiogenesis-related factors in the model rats (Mod) relative to normal rats (Nor), including Huiyang extract (HE), Shengji extract (SE), Huiyang Shengji extract (HSE) and HSE associated with acupuncture (Ac-HSE). We then performed NMR-based metabolomic analyses on skin tissues of the Nor, Mod, HSE-treated, Ac-HSE-treated rats to address metabolic mechanisms underlying these effects.RESULTS:These treatments up-regulated expressions of two angiogenesis-related factors VEGF and CD31, and improved efficacy of healing DSUs, in which HSE and Ac-HSE exhibited the most significant effects. Compared with Mod, HSE and Ac-HSE groups shared four characteristic metabolites (lactate, histidine, succinate and acetate) and four significantly altered metabolic pathways with Nor. Both HSE and Ac-HSE treatments could partly reverse the metabolically disordered pathological state of DSUs to the normal state. They might improve wound healing through promoting glucose metabolism, BCAAs metabolism, and enhancing antioxidant capacity and angiogenesis in DSU tissues. Ac-HSE significantly enhanced wound healing rates compared to HSE, potentially owing to significant capacities of enhancing anti-oxidation and angiogenesis and interfering three more metabolic pathways.CONCLUSIONS:This work provides a mechanistic understanding of the healing effects of the HSE and Ac-HSE treatments on DSUs, is of benefit to improvements of the HSF treatments for clinically healing chronic non-healing wounds.

Cellular senescence is a physiological process reacting to stimuli, in which cells enter a state of irreversible growth arrest in response to adverse consequences associated with metabolic disorders. Molecular mechanisms underlying the progression of cellular senescence remain unclear. Here, we established a replicative senescence model of human umbilical vein endothelial cells (HUVEC) from passage 3 (P3) to 18 (P18), and performed biochemical characterizations and NMR-based metabolomic analyses. The cellular senescence degree advanced as the cells were sequentially passaged in vitro, and cellular metabolic profiles were gradually altered. Totally, 8, 16, 21 and 19 significant metabolites were primarily changed in the P6, P10, P14 and P18 cells compared with the P3 cells, respectively. These metabolites were mainly involved in 14 significantly altered metabolic pathways. Furthermore, we observed taurine retarded oxidative damage resulting from senescence. In the case of energy deficiency, HUVECs metabolized neutral amino acids to replenish energy, thus increased glutamine, aspartate and asparagine at the early stages of cellular senescence but decreased them at the later stages. Our results indicate that cellular replicative senescence is closely associated with promoted oxidative stress, impaired energy metabolism and blocked protein synthesis. This work may provide mechanistic understanding of the progression of cellular senescence.

With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development.

The emergence of antibiotic-resistant Mycobacterium Tuberculosis (Mtb) infections compels new treatment strategies, of which targeting trans-translation is promising. During the trans-translation process, the ribosomal protein S1 (RpsA) plays a key role, and the Ala438 mutant is related to pyrazinamide (PZA) resistance, which shows its effects after being hydrolysed to pyrazinoic acid (POA). In this study, based on the structure of the RpsA C-terminal domain (RpsA-CTD) and POA complex, new compounds were designed. After being synthesized, the compounds were tested in vitro with saturation transfer difference (STD), fluorescence quenching titration (FQT) and chemical shift perturbation (CSP) experiments. Finally, six of the 17 new compounds have high affinity for both RpsA-CTD and its Ala438 deletion mutant. The active compounds provide new choices for targeting trans-translation in Mtb, and the analysis of the structure-activity relationships will be helpful for further structural modifications based on derivatives of 2-((hypoxanthine-2-yl)thio)acetic acid and 2-((5-hydroxylflavone-7-yl)oxy)acetamide.

Aim: CDK4/6 have critical roles in the early stage of the cell cycle. CDK2 acts later in the cell cycle and has a considerably broader range of protein substrates, some of which are essential for normal cell proliferation. Therefore, increasing the selectivity of cyclin-dependent kinase (CDK) inhibitors is critical. Methodology: In this study, we construct a versatile, specific CDK4 pharmacophore model that not only matches well with 8119 of the reported 9349 CDK4/6 inhibitors but also differentiates from the CDK2 pharmacophore. Results & Conclusion: we demonstrate the activity and selectivity determinants of CDK4/6 selective inhibitors based on the CDK4 pharmacophore model. Finally, we propose the future optimization strategy for CDK4/6 selective inhibitors, providing a theoretical basis for further research and development of CDK4/6 selective inhibitors. [GRAPHICS] . Graphical abstract According to our CDK4 feature model, four decisive factors (capability as a hydrogen bond donor and acceptor, hydrophobicity, electrostatic potential) are highly desirable for potent inhibitory activity and selectivity. At present, most CDK4/6 selective inhibitors have similar skeletons and branches. The structure-activity relationship revealed by the 3D quantitation structure-activity relationships pharmacophore study is illustrated in the following picture. The key hydrogen bond with the hinge region (Val96) is indispensable for activity, although it does not play a major role in selectivity. Along the dotted line, we can design a hetero atom-containing group to form a hydrogen bond interaction with the Lys35(CDK4) at the bottom of the binding site. Selectivity is not the effect of a particular residue, but a cumulative effect. A unique hydrogen bonding between the side chain of His95 and the CDK4 inhibitor could improve the selectivity. There is no steric hindrance (Thr102) at the entrance of the binding pocket of CDK4, and the branch A of the scaffold could be designed to be larger such that it is appropriately positively charged and extends to the 'top' of the binding site (Asp99) to improve selectivity.

The programmed cell death ligand protein 1 (PD-L1) is a member of the B7 protein family and consists of 290 amino acid residues. The blockade of the PD-1/PD-L1 immune checkpoint pathway is effective in tumor treatment. Results: Two pharmacophore models were generated based on peptides and small molecules. Hypo 1A consists of one hydrogen bond donor, one hydrogen bond acceptor, two hydrophobic points and one aromatic ring point. Hypo 1B consists of one hydrogen bond donor, three hydrophobic points and one positive ionizable point. Conclusions: The pharmacophore model consisting of a hydrogen bond donor, hydrophobic points and a positive ionizable point may be helpful for designing small-molecule inhibitors targeting PD-L1.

Future Medicinal ChemistryVol. 11, No. 14 CommentaryFree AccessNeural networks in drug discovery: current insights from medicinal chemistsYinqiu Xu, Xuanyi Li, Hequan Yao & Kejiang LinYinqiu XuDepartment of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, PR China, Xuanyi LiDepartment of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, PR China, Hequan YaoDepartment of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, PR China & Kejiang Lin*Author for correspondence: E-mail Address: link@cpu.edu.cnDepartment of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, PR ChinaPublished Online:9 Jul 2019https://doi.org/10.4155/fmc-2019-0118AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit Keywords: artificial intelligencedrug discoverymachine learningneural networksNeural networks in drug discoveryIn recent years, neural networks (NNs) have become too effective to be mistaken as an equivalent of artificial intelligence (AI) [1]. Similar to the other machine learning (ML) algorithms for realizing artificial intelligence, NNs identify rules from samples. To some degree NNs appear to be powerful mathematical formulas, which accurately describe the relationships between independent variables and dependent variables, and the training of NNs resembles the process of approximating the formulas. According to the universal approximation theorem, NNs with simple architecture can approximate many functions [2]. Though the main idea of NNs is easy to understand, the specific mechanisms are delicate and complicated (as previously described in [3,4]).Furthermore, the relationships between the structure and activity in drug discovery can be described with formulas, which has resulted in the application of NNs to drug discovery. The dependent variables of drug discovery can be discrete (such as whether a compound is safe or not) or continuous (such as IC50 values), and the corresponding NNs can be trained as qualitative models or quantitative models. Due to the powerful ability of NNs to adapt, NNs always show better performance than other models (as previously reviewed in [1,4,5]). From the perspective of independent variables that describe molecules, NNs accept many ways of describing ligands [5], receptors and receptor–ligand interactions. In this way they cover the areas of ligand-based drug design, de novo drug design and receptor-based drug design.The structures of molecules determine their properties, a concept which kept in mind by medicinal chemists and reflects the importance of molecular structures. Similarly, before preparing in silico models for drug discovery, how molecular structures are represented affects the quality of the models. Traditionally, molecular descriptors and fingerprints have been used to represent structures. However, they are preprocessed information rather than raw information of structures. Due to the preprocessing, molecular descriptors and fingerprints may not represent structures accurately and comprehensively. Though many kinds of NNs exist, such as genetic NNs, self-organizing maps and radial basis function NNs, they can hardly deal with the raw information of molecular structures. With the development of recurrent neural networks (RNNs), convolutional neural networks (CNNs) and molecular graph-based neural networks (MGNNs), raw information indicating molecular structures can be input into models based on NNs, which helps the machines analyze the rules directly and comprehensively.Considering models based on NNs as tools for different tasks, the tools have been being developed from simple NNs to NNs with delicate architectures and diverse components. Artificial neural networks (ANNs) are the most basic components that combine different independent variables into more or fewer new variables, which can be further combined as subsequent variables or final outputs. RNNs are components that combine ANNs recursively so that their independent variables can be propagated dynamically, which make RNNs good at dealing with sequential inputs such as the simplified molecular input line entry specification (SMILES). Inside CNNs, different ANNs are used to combine local neighboring variables into different new variables. The idea of CNNs is shared by MGNNs, but MGNNs are implemented differently to adapt to non-Euclidean data. The components mentioned above are being updated. Extra ANNs can be used to control, read and write the inner variables of NNs so that – to some degree – the updated NNs will have a memory. Meanwhile, the elaborate design of the inner elements of those NNs makes the NNs more adaptive. Take CNNs as an example. The dynamic k-max pooling provides CNNs with new potential to deal with sequential inputs, and the global average pooling further alleviates the overfitting and the effects of the input size. When the outputs of a single NN are set as new inputs rather than final results, the functions of models can be enlarged. The first NN can be set as an encoder, which converts its appropriate inputs into new representations and the new representations can be used by another NN for qualitative or quantitative tasks. More importantly, the new representations can be decoded by the latter NN, which can be used for generating new molecules. The simple process of encoding and decoding just regenerates the inputs, but introducing the adversarial mechanism and reparameterization makes the process different, which results in adversarial autoencoders and variational autoencoders. In addition to encoders and decoders, NNs can be set as generators and discriminators as well, which are two necessary parts of generative adversarial networks. After preparing the architecture of NNs, models that are more practical can be trained according to the learning methods. Transfer learning and one-shot learning are designed for tasks that have limited samples, and having enough samples is usually a problem in drug discovery. Reinforcement learning is a dynamic process during which models can be trained according to desired properties, and the process is exactly appropriate for generating SMILES sequences. The mentioned tools based on NNs accept raw information of molecules for traditional qualitative or quantitative tasks and connect in different ways, which provide colorful choices for drug discovery [5].Reliability versus noveltyAppropriate architectures, optimized hyperparameters and accurate samples are three important factors for training good models. The first two factors are adjusted through careful validations, while the samples are usually prepared before training.During the training process of NNs, the inner weights and biases of NNs are adjusted according to the inputted data. More data for a model means that the inner parameters of the model are more adaptive to various samples, so the model can be well generalized when dealing with new samples. Abundant samples make models reliable, which are needed by models to predict molecular properties such as water solubility and toxicity. However, a dilemma comes with an enlarged dataset, especially for medicinal chemists focusing on discovering new active compounds. In drug discovery, abundant existing samples for a target indicate that less space is available for further research and rigorous intellectual properties. Whether models based on NNs can discover desired molecules remains to be tested in practice.Recently, a comparison between models based on NNs and other ML algorithms further emphasized the contradiction between reliability and novelty [6]. The models were found to provide worse predictions when dealing with newer samples, and models based on NNs did not provide much improvement when compared with several ML models. Well-designed architectures and optimized hyperparameters may not improve the performance of NNs much. The best solution is to supply stranger samples, which could result in the loss of novelty. Although there are models that are designed for tasks with limited samples, their performance remains uncertain. For instance, the performance of models based on one-shot learning was found to be unsatisfactory for virtual screening tasks [7]. Classifying and scoring docking results is also a solution to the contradiction. However, the NN-based models for these tasks significantly depend on the docking methods, and to some degree, the docking methods may limit the potential of these models.From the perspective of medicinal chemists, NNs provide effective tools to choose compounds with better drug-likeness for clinical research, but more cases in practice are necessary to prove the ability of NNs in innovative tasks such as virtual screening and predicting potential targets.More models in silico but limited applications in realityAlong with the fast growth of models, some details have been noticed to help improve models. For example, the effects of the hyperparameters [8], the quality of the dataset [9] and the diversity of SMILES sequences [10] have been mentioned. However, medicinal chemists may still get confused when choosing an appropriate model. Since sometimes complex models may not bring satisfactory results, comparing the metrics between different models will help alleviate the confusion, and benchmarks were proposed for traditional tasks [11] and de novo molecule generation [12].Evaluations that use metrics aim to predict rather than to represent the real values of models, and the ability to use models for real tasks is what concerns medicinal chemists. Take the models for de novo molecule generation as an example. It is easy for these models to achieve satisfactory metrics, but the generated molecules may not be stable or chemically accessible. With respect to practical applications, solving a real problem in drug discovery is more important than evaluations in silico since drug discovery is never an easy task. Although most models were validated and tested theoretically, the good news is that there are now several successful cases that only depend on simple algorithms. Active compounds targeting nuclear receptors [13] and kinases [14,15] were discovered using NN-based models. Meanwhile, anticancer peptides were successfully designed using RNNs [16]. In these cases, models based on NNs prove their practical value.NNs & automated drug discoveryIt seems that NNs are now aiding drug discovery, but it is reasonable to foresee that models based on NNs will automatically perform drug discovery. Automatic devices for synthesis, analysis and biological tests represent a trend in the industry [17], though the devices just perform as they are designed. Combined with ML, those devices may determine their actions on their own so that an automatic 'design-make-test' cycle, which is a feedback loop, can be fulfilled in drug discovery [18]. It is worth noting that the loop has been successfully implemented using ML [19], and four reactions were successfully discovered. Among the diverse ML algorithms, NNs have proved their ability in many stages of drug discovery, such as the automatic design of new molecules and the design of synthetic routes [20]. Furthermore, the automatic cycle is also a dynamic process, which means that reinforcement learning may also help models based on NNs to learn rules and design new drugs. More importantly, NNs accept various kinds of data as inputs, which include both abstract information and intuitive representations such as words and images; further, compared with other ML algorithms, this characteristic enables NNs to analyze the results of their actions directly and conveniently. Based on those advantages of NNs, it is reasonable to infer that NNs may be better integrated into the automatic feedback loop in drug discovery.ConclusionOverall, it seems that medicinal chemists have not kept pace with the rapid updates in NN models. Although new ideas and models keep emerging for virtual drug discovery, it is hard to evaluate which one is the best for applications. More choices in silico do not represent the proper scope of the applications, and impressive practical cases are still limited. The main cause may be the uncertainty of NNs in dealing with difficult tasks with limited samples, which are usually the main focus of medicinal chemists. Cooperation between researchers in cheminformatics and medicinal chemists will help identify potential problems and create more effective models, which may result in bringing impressive results. Meanwhile, the cooperation will accelerate the integration of NNs and the 'design-make-test' cycle, so that automated drug discovery can be realized in the future.Financial & competing interests disclosureThis work was supported by funds from the National Key R&D Program of China (2018YFC0311005), 'Double First-Class' University Project, China (CPU2018GY15) and Key Laboratory of Spectrochemical Analysis & Instrumentation (Xiamen University), Ministry of Education, China (SCAI1802). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.The authors thank the American Journal Experts (AJE), Durham, NC, USA, for their English language editing.References1. Carpenter KA, Cohen DS, Jarrell JT, Huang X. Deep learning and virtual drug screening. Future Med. 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The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.The authors thank the American Journal Experts (AJE), Durham, NC, USA, for their English language editing.PDF download

Aim: Parathyroid hormone-1 receptor (PTH1R) is a member of B G protein-coupled receptors. The agonistic activation of the PTH1R results in the production and secretion of osteoclast-stimulating cytokines while antagonists may be used to treat bone metastases, hypercalcemia, cachexia and hyperparathyroidism. Results: We built pharmacophore models and investigated the characteristics of PTH1R agonists and antagonists. The agonist model consists of three hydrophobic points, one hydrogen bond acceptor and one positive ionizable point. The antagonist model consists of one hydrogen bond donor and three hydrophobic points. Conclusion: The features of the two models are similar, but the hydrogen bond acceptor, which is the main difference between PTH1R agonists and antagonists, suggests it may be essential for the agonist.

Introduction: Fibrotic disorders are a leading cause of morbidity and mortality; hence effective treatments are still vigorously sought. AdipoRs (AdipoR1 and Adipo2) are responsible for the antifibrotic effects of adiponectin (APN). APN exerts antifibrotic effects by binding to its receptors. APN concentration and AdipoR expression are closely associated with fibrotic disorders. Decreased AdipoR expression may reduce APN-AdipoR signaling, while the upregulation of AdipoR expression may restore the anti-fibrotic effects of APN. Loss of APN signaling exacerbates fibrosis in vivo and in vitro. Areas covered: We assess the relationship between APN and fibrotic disorders, the structure of receptors for APN and the pathways accounting for APN or its analogs blocking fibrotic disorders. This article also discusses designed APN products and their therapeutic prospects for fibrotic disorders. Expert opinion: AdipoRs have a critical role in blocking fibrosis. The development of small-molecule agonists toward this target represents a valid drug development pathway.