Antibody informatics for drug discovery.

More and more antibody therapeutics are being approved every year, mainly due to their high efficacy and antigen selectivity. However, it is still difficult to identify the antigen, and thereby the function, of an antibody if no other information is available. There are obstacles inherent to the antibody science in every project in antibody drug discovery. Recent experimental technologies allow for the rapid generation of large-scale data on antibody sequences, affinity, potency, structures, and biological functions; this should accelerate drug discovery research. Therefore, a robust bioinformatic infrastructure for these large data sets has become necessary. In this article, we first identify and discuss the typical obstacles faced during the antibody drug discovery process. We then summarize the current status of three sub-fields of antibody informatics as follows: (i) recent progress in technologies for antibody rational design using computational approaches to affinity and stability improvement, as well as ab-initio and homology-based antibody modeling; (ii) resources for antibody sequences, structures, and immune epitopes and open drug discovery resources for development of antibody drugs; and (iii) antibody numbering and IMGT. Here, we review "antibody informatics," which may integrate the above three fields so that bridging the gaps between industrial needs and academic solutions can be accelerated. This article is part of a Special Issue entitled: Recent advances in molecular engineering of antibody.

[Drug discovery research using AI and data science: analyzing literature information to understand pathological mechanisms].

Recent rapid progress in big data and breakthrough AI technologies have brought about significant changes in the medical field as well. Although biomedical literature databases contain so many articles that it is impossible to read them all, AI technology based on neural networks has dramatically advanced and is now able to efficiently process such vast amounts of literature information in a short time. Since drug discovery research requires up-to-date and extensive knowledge of various disciplines, it is necessary to proactively incorporate AI technology to seamlessly obtain the information needed. In this article, we introduce our effort to use the rapidly growing literature data and the latest AI technologies to drug discovery research. Conventional search engines take an enormous amount of time to identify and understand sentences describing the subject matter of interest in the retrieved articles. We developed and validated our new search tool that not only has a conventional keyword search function, but also enables conceptual search for disease mechanisms using sentences. We will also describe problems that we have identified through actual use of the tool. Finally, since literature data is expected to increase and efforts to determine how to efficiently analyze and obtain desired findings using AI will become even more active, we will discuss expectations for future technological advances and issues that need to be resolved.

ARHGAP-RhoA signaling provokes homotypic adhesion-triggered cell death of metastasized diffuse-type gastric cancer.

Genetic alteration of Rho GTPase-activating proteins (ARHGAP) and GTPase RhoA is a hallmark of diffuse-type gastric cancer and elucidating its biological significance is critical to comprehensively understanding this malignancy. Here, we report that gene fusions of ARHGAP6/ARHGAP26 are frequent genetic events in peritoneally-metastasized gastric and pancreatic cancer. From the malignant ascites of patients, we established gastric cancer cell lines that spontaneously gain hotspot RHOA mutations or four different ARHGAP6/ARHGAP26 fusions. These alterations critically downregulate RhoA-ROCK-MLC2 signaling, which elicits cell death. Omics and functional analyses revealed that the downstream signaling initiates actin stress fibers and reinforces intercellular junctions via several types of catenin. E-cadherin-centered homotypic adhesion followed by lysosomal membrane permeabilization is a pivotal mechanism in cell death. These findings support the tumor-suppressive nature of ARHGAP-RhoA signaling and might indicate a new avenue of drug discovery against this refractory cancer.

Are there differences between launched drugs, clinical candidates, and commercially available compounds?

To clarify the differences between commercially available compounds, clinical candidates, and launched drugs with regard to distribution of physicochemical properties and to characterize the correlation between physicochemical properties, we conducted analyses on physicochemical descriptors of commercially available compounds, clinical candidates, and launched drugs. Initial analysis of the marginal distribution of each physicochemical property showed that the distribution of commercially available compounds obeys a more normal distribution than that of launched drugs and clinical candidates. In addition, we calculated correlation coefficient values between values of physicochemical properties and found little similarity between values of clinical candidates and those of commercially available compounds, while observing marked similarity between values of clinical candidates and those of launched drugs. We also analyzed joint distribution for two physicochemical properties, with results showing that, similar to marginal distribution, the joint distribution of commercially available compounds obeys a more normal distribution than that of launched drugs and clinical candidates. We then assessed items using the Nagahara method, originally developed by one of this study's authors. Results showed that the probability distribution of molecular weight and log P for commercially available compounds was much narrower than that of launched drugs and clinical candidates. In conclusion, clinical candidates are more similar to launched drugs than to commercially available compounds with regard to marginal distribution, joint distribution, and correlation coefficients. These findings provide deeper insight regarding the concept of "druglikeness".

Agreement of drug discovery data with Benford's law.

The ever-increasing rate of drug discovery data has complicated data analysis and potentially compromised data quality due to factors such as data handling errors. Parallel to this concern is the rise in blatant scientific misconduct. Combined, these problems highlight the importance of developing a method that can be used to systematically assess data quality. Benford's law has been used to discover data manipulation and data fabrication in various fields. In the authors' previous studies, it was demonstrated that the distribution of the corresponding activity and solubility data followed Benford's law distribution. It was also shown that too intense a selection of training data sets of regression model can disrupt Benford's law. Here, the authors present the application of Benford's law to a wider range of drug discovery data such as microarray and sequence data. They also suggest that Benford's law could also be applied to model building and reliability for structure-activity relationship study. Finally, the authors propose a protocol based on Benford's law which will provide researchers with an efficient method for data quality assessment. However, multifaceted quality control such as combinatorial use with data visualization may also be needed to further improve its reliability.

Scaffold hopping in drug discovery using inductive logic programming.

In chemoinformatics, searching for compounds which are structurally diverse and share a biological activity is called scaffold hopping. Scaffold hopping is important since it can be used to obtain alternative structures when the compound under development has unexpected side-effects. Pharmaceutical companies use scaffold hopping when they wish to circumvent prior patents for targets of interest. We propose a new method for scaffold hopping using inductive logic programming (ILP). ILP uses the observed spatial relationships between pharmacophore types in pretested active and inactive compounds and learns human-readable rules describing the diverse structures of active compounds. The ILP-based scaffold hopping method is compared to two previous algorithms (chemically advanced template search, CATS, and CATS3D) on 10 data sets with diverse scaffolds. The comparison shows that the ILP-based method is significantly better than random selection while the other two algorithms are not. In addition, the ILP-based method retrieves new active scaffolds which were not found by CATS and CATS3D. The results show that the ILP-based method is at least as good as the other methods in this study. ILP produces human-readable rules, which makes it possible to identify the three-dimensional features that lead to scaffold hopping. A minor variant of a rule learnt by ILP for scaffold hopping was subsequently found to cover an inhibitor identified by an independent study. This provides a successful result in a blind trial of the effectiveness of ILP to generate rules for scaffold hopping. We conclude that ILP provides a valuable new approach for scaffold hopping.