LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-19

Unleashing the power of generative AI in drug discovery

Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.

Molecular Mechanism of Chronic Viral and Non-Viral Liver Diseases

In this Special Issue, “Molecular Mechanism of Chronic Viral and Non-viral Liver Diseases”, invaluable articles have been published [...]

Drug Screening for Hepatitis A Virus (HAV): Nicotinamide Inhibits c-Jun Expression and HAV Replication

Hepatitis A virus (HAV) infection often causes acute hepatitis, which results in a case fatality rate of 0.2% and fulminant hepatitis in 0.5% of cases. However, no specific potent anti-HAV drug is available on the market to date. In the present study, we focused on inhibition of HAV internal ribosomal entry site (IRES)-mediated translation and investigated novel therapeutic drugs through drug repurposing by screening for inhibitors of HAV IRES-mediated translation and cell viability using a reporter assay and cell viability assay, respectively. The initial screening of 1,158 drugs resulted in 77 candidate drugs. Among them, nicotinamide significantly inhibited HAV HA11-1299 genotype IIIA replication in Huh7 cells. This promising drug also inhibited HAV HM175 genotype IB subgenomic replicon and HAV HA11-1299 genotype IIIA replication in a dose-dependent manner. In the present study, we found that nicotinamide inhibited the activation of activator protein 1 (AP-1) and that knockdown of c-Jun, which is one of the components of AP-1, inhibited HAV HM175 genotype IB IRES-mediated translation and HAV HA11-1299 genotype IIIA and HAV HM175 genotype IB replication. Taken together, the results showed that nicotinamide inhibited c-Jun, resulting in the suppression of HAV IRES-mediated translation and HAV replication, and therefore, it could be useful for the treatment of HAV infection. IMPORTANCE Drug screening methods targeting HAV IRES-mediated translation with reporter assays are attractive and useful for drug repurposing. Nicotinamide (vitamin B3, niacin) has been shown to effectively inhibit HAV replication. Transcription complex activator protein 1 (AP-1) plays an important role in the transcriptional regulation of cellular immunity or viral replication. The results of this study provide evidence that AP-1 is involved in HAV replication and plays a role in the HAV life cycle. In addition, nicotinamide was shown to suppress HAV replication partly by inhibiting AP-1 activity and HAV IRES-mediated translation. Nicotinamide may be useful for the control of acute HAV infection by inhibiting cellular AP-1 activity during HAV infection processes.