Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.

Since the Simplified Molecular Input Line Entry System (SMILES) is oriented to the atomic-level representation of molecules and is not friendly in terms of human readability and editable, however, IUPAC is the closest to natural language and is very friendly in terms of human-oriented readability and performing molecular editing, we can manipulate IUPAC to generate corresponding new molecules and produce programming-friendly molecular forms of SMILES. In addition, antiviral drug design, especially analogue-based drug design, is also more appropriate to edit and design directly from the functional group level of IUPAC than from the atomic level of SMILES, since designing analogues involves altering the R group only, which is closer to the knowledge-based molecular design of a chemist. Herein, we present a novel data-driven self-supervised pretraining generative model called "TransAntivirus" to make select-and-replace edits and convert organic molecules into the desired properties for design of antiviral candidate analogues. The results indicated that TransAntivirus is significantly superior to the control models in terms of novelty, validity, uniqueness, and diversity. TransAntivirus showed excellent performance in the design and optimization of nucleoside and non-nucleoside analogues by chemical space analysis and property prediction analysis. Furthermore, to validate the applicability of TransAntivirus in the design of antiviral drugs, we conducted two case studies on the design of nucleoside analogues and non-nucleoside analogues and screened four candidate lead compounds against anticoronavirus disease (COVID-19). Finally, we recommend this framework for accelerating antiviral drug discovery.

We construct a protein–protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.

Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug candidates are usually analogous to or derived from the molecular structures of NC. In order to express the relationship physically realistically using a computer, it is essential to have a molecular descriptor set that can adequately represent the characteristics of the molecular structures belonging to the NC’s chemical space. Although several topological descriptors have been developed to describe the physical, chemical, and biological properties of organic molecules, especially synthetic compounds, and have been widely used for drug discovery researches, these descriptors have limitations in expressing NC-specific molecular structures. To overcome this, we developed a novel molecular fingerprint, called Natural Compound Molecular Fingerprints (NC-MFP), for explaining NC structures related to biological activities and for applying the same for the natural product (NP)-based drug development. NC-MFP was developed to reflect the structural characteristics of NCs and the commonly used NP classification system. NC-MFP is a scaffold-based molecular fingerprint method comprising scaffolds, scaffold-fragment connection points (SFCP), and fragments. The scaffolds of the NC-MFP have a hierarchical structure. In this study, we introduce 16 structural classes of NPs in the Dictionary of Natural Product database (DNP), and the hierarchical scaffolds of each class were calculated using the Bemis and Murko (BM) method. The scaffold library in NC-MFP comprises 676 scaffolds. To compare how well the NC-MFP represents the structural features of NCs compared to the molecular fingerprints that have been widely used for organic molecular representation, two kinds of binary classification tasks were performed. Task I is a binary classification of the NCs in commercially available library DB into a NC or synthetic compound. Task II is classifying whether NCs with inhibitory activity in seven biological target proteins are active or inactive. Two tasks were developed with some molecular fingerprints, including NC-MFP, using the 1-nearest neighbor (1-NN) method. The performance of task I showed that NC-MFP is a practical molecular fingerprint to classify NC structures from the data set compared with other molecular fingerprints. Performance of task II with NC-MFP outperformed compared with other molecular fingerprints, suggesting that the NC-MFP is useful to explain NC structures related to biological activities. In conclusion, NC-MFP is a robust molecular fingerprint in classifying NC structures and explaining the biological activities of NC structures. Therefore, we suggest NC-MFP as a potent molecular descriptor of the virtual screening of NC for natural product-based drug development.

The prevalence of a novel β-coronavirus (SARS-CoV-2) was declared as a public health emergency of international concern on 30 January 2020 and a global pandemic on 11 March 2020 by WHO. The spike glycoprotein of SARS-CoV-2 is regarded as a key target for the development of vaccines and therapeutic antibodies. In order to develop anti-viral therapeutics for SARS-CoV-2, it is crucial to find amino acid pairs that strongly attract each other at the interface of the spike glycoprotein and the human angiotensin-converting enzyme 2 (hACE2) complex. In order to find hot spot residues, the strongly attracting amino acid pairs at the protein–protein interaction (PPI) interface, we introduce a reliable inter-residue interaction energy calculation method, FMO-DFTB3/D/PCM/3D-SPIEs. In addition to the SARS-CoV-2 spike glycoprotein/hACE2 complex, the hot spot residues of SARS-CoV-1 spike glycoprotein/hACE2 complex, SARS-CoV-1 spike glycoprotein/antibody complex, and HCoV-NL63 spike glycoprotein/hACE2 complex were obtained using the same FMO method. Following this, a 3D-SPIEs-based interaction map was constructed with hot spot residues for the hACE2/SARS-CoV-1 spike glycoprotein, hACE2/HCoV-NL63 spike glycoprotein, and hACE2/SARS-CoV-2 spike glycoprotein complexes. Finally, the three 3D-SPIEs-based interaction maps were combined and analyzed to find the consensus hot spots among the three complexes. As a result of the analysis, two hot spots were identified between hACE2 and the three spike proteins. In particular, E37, K353, G354, and D355 of the hACE2 receptor strongly interact with the spike proteins of coronaviruses. The 3D-SPIEs-based map would provide valuable information to develop anti-viral therapeutics that inhibit PPIs between the spike protein of SARS-CoV-2 and hACE2.

Inhibitors to interfere protein-protein interactions (PPI) between programmed cell death 1 (PD-1) and programmed death ligand-1 (PD-L1) block evasion of cancers from immune surveillance. Analyzing hot spot residues in PPI is important for small-molecule drug development. In order to find out hot spots on PPI interface in PD-1/PD-L1 complex, we analyzed PPI in PD-1/PD-L1 with a new analysis method, 3-dimensional scattered pair interactions energies (3D-SPIEs), which assorts significant interactions with fragment molecular orbital (FMO) method. By additionally analyzing PPI in PD-1/antibody and PD-L1/antibody complexes, and small-ligand interactions in PD-L1/peptide and PD-L1/small-molecule complexes, we narrowed down the hot spot region with 3D-SPIEs-based interaction map, which integrates PPI and small-ligand interactions. Based on the map, there are two hot spot regions in PPI of PD-1/PD-L1 and the first hot spot region is important for inhibitors. In particular, _LY56, _LE58, and _LN66 in the first hot spot of PD-L1 are important for PD-L1-antibodies and small-inhibitors in common, while _LM115 is important for small-inhibitors. Therefore, the 3D-SPIEs-based map would provide valuable information for designing new small-molecule inhibitors to inhibit PPI of PD-1/PD-L1 and the FMO/3D-SPIEs method provides an effectual tool to understand PPI and integrate PPI and small-ligand interactions at a quantum mechanical level.