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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.

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.

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.

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.

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.

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 ...

It has been observed that synonymous substitution rates vary among genes in various organisms, although the cause of the variation is unresolved. At the intragenic level, however, the variation of synonymous substitutions is somewhat controversial. By developing a rigorous statistical test and applying the test to 418 homologous gene pairs between mouse and rat, we found that more than 90% of gene pairs showed a statistical significance in intragenic variation of synonymous substitution rates. Moreover, by examining all conceivable possibilities for the cause of the variation, we successfully found that intragenic variation of synonymous substitutions in mammalian genes is caused mainly by a nonrandom mutation due to the methylation of CpG dinucleotides rather than by functional constraints.

In the protein-coding gene, nucleotide substitutions are classified into the synonymous and nonsynonymous substitutions. The synonymous substitution in a gene is defined as those not causing amino acid changes in the encoding protein, while the nonsynonymous substitution is defined as those causing amino acid changes in it. The synonymous substitution, by definition, is free from functional constraints of a protein contrary to the nonsynonymous substitution that is essentially constrained by protein function. Therefore, it is expected that for a given gene, the rate of synonymous substitution is constant over the nucleotide sites as long as mutation rate does not vary with the sites. It is also anticipated that synonymous substitutions take place more frequently than nonsynonymous substitutions. It follows that the difference between the numbers of synonymous and nonsynonymous substitutions reflects the degree of functional importance for a protein, meaning that the difference is larger as the degree is greater. I find these properties of synonymous and nonsynonymous substitutions useful for evaluating the functional importance for subunits as well as domains of a protein. Moreover, I could successfully show that the rate of synonymous substitution is variable with site not only among genes but also within a gene. I also find that for mammalian species, the intragenic variation of synonymous substitutions is mainly caused by mutation that preferentially occurs in non-randomly distributed CpG dinucleotides in a gene. In Chapter I, I outline my thesis, placing particular emphasis on the motivation and purpose of my study. In Chapter II, for nicotinic acetylcholine receptor subunit genes of different species, I examined the degree of functional importance of subunits by conducting comparative analysis of the numbers of synonymous and nonsynonymous substitutions. It is known that nicotinic acetylcholine receptor is divided into two types, the muscular and nervous. The muscular type is composed of five subunits, a1, a1, b1, g and e. There are four trans-membrane regions, M 1 - M4, in the receptor molecule. The structure of the nervous type is not well elucidated, but is known to be composed of a(2-9) and b(2-4) subunits. In particular, by computing the ratio (f) of the number of nonsynonymous substitutions to that of synonymous substitutions, I showed that the a1 subunit gene was the lowest in f value among the subunit genes in the muscle system, and so was a7 subunit gene in the nervous system. This result indicates that the two subunit genes in the two tissues have been subject to strong functional constraints in evolution. In fact, it is known that the two subunits of the receptor protein have crucial functions; a1 subunit has binding sites to the ligand of the receptor, and a7-containing receptor regulates releasing the transmitter, acetylcholine. Moreover, the window analysis of f values shows that strong functional constraints have operated on M2 region in all the five muscle subunits. It is noted that M2 region is a part of the ion channel structure in the receptor molecule. Therefore, f value is shown to be useful for evaluating the degree of functional importance of not only a gene but also subregions within a gene. In Chapter III, I conducted a statistical test to examine whether the rate of synonymous substitution varies within a gene, by using 418 homologous gene pairs from Rattus norvegicus and Mus musculus and 84 orthologous gene pairs from the whole bacterial genomes of Mycoplasma genitalium and Mycoplasma pneumoniae. As a result, more than 90% of gene pairs for both comparisons are demonstrated to show a significant intragenic variation of synonymous substitution rate. By examining all conceivable possibilities for the cause of the intragenic variation of synonymous substitution rates, I finally found a significant correlation between synonymous substitution rates and the frequency of CpG dinucleotides in rodents. These findings suggest that intragenic variation of synonymous substitutions in mammals is caused mainly by a mutation due to methylation of CpG dinucleotides which are unevenly distributed in the genome. In Chapter IV, I described the summary and conclusion of the present study, and I also discussed the future development of this line of study.