Large Language Models in Plant Biology

Large Language Models (LLMs), such as ChatGPT, have taken the world by storm and have passed certain forms of the Turing test. However, LLMs are not limited to human language and analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multi-purpose prediction tools able to explain cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom

Transcripts Per Million Ratio: a novel batch and sample control method over an established paradigm

Current popular methods in literature of RNA sequencing normalisation do not account for gene length when compared across samples, whilst adjusting for count biases in the data. This creates a gap in the normalisation as bigger genes in RNA sequencing accumulate more reads due to shotgun sequencing methods. As a result, the proportions of these reads inter-sample are not properly accounted for in current normalisation methods. Alternatively, methods which account for gene length do not account for the pan-sample biases in the data by accounting for a central read average. Thus, in order to fill in the gap in the literature, we propose a novel method of Transcripts Per Million Ratio and its relatives in RNA-sequencing differential expression normalisation that can be used in different conditions, which takes into account the gene length as well as relative expression in normalisation

In silico repurposed drugs against monkeypox virus

Monkeypox is an emerging epidemic of concern. The disease is caused by the monkeypox virus and an increasing global incidence with a 2022 outbreak that has spread to Europe amid the COVID-19 pandemic. The new outbreak is associated with novel, previously undiscovered mutations and variants. Currently, the US Food and Drug Administration (FDA) approved poxvirus treatment involves the use of tecovirimat. However, there is otherwise limited pharmacopoeia and research interest in monkeypox. In this study, virtual screening and molecular dynamics were employed to explore the potential repurposing of multiple drugs previously approved by the FDA or other jurisdictions for other applications. Several drugs are predicted to tightly bind to viral proteins, which are crucial in viral replication, including molecules which show high potential for binding the monkeypox D13L capsid protein, whose inhibition has previously been demonstrated to suppress viral replication.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Skin Research Institute of Singapore (SRIS)Published versionThis work is supported by the Singapore Ministry of Education (MOE) Tier 1 grant RG27/21 and Tier 2 grant MOE-T2EP30120-0007. Thomas Larry Dawson and Hilbert Lam Yuen In weresupported by funding from the Agency for Science, Technology and Research (A*STAR) and A*STARBMRC EDB IAF-PP grants (H17/01/a0/004, Skin Research Institute of Singapore, and H18/01a0/016,Asian Skin Microbiome Program)

Application of variational graph encoders as an effective generalist algorithm in computer-aided drug design

Although there has been considerable progress in molecular property prediction in computer-aided drug design, there is a critical need to have fast and accurate models. Many of the currently available methods are mostly specialize in predicting specific properties, leading to the use of many models side-by-side that lead to impossibly high computational overheads for the common researcher. Henceforth, the authors propose a single, generalist unified model exploiting graph convolutional variational encoders that can simultaneously predict multiple properties such as absorption, distribution, metabolism, excretion and toxicity, target-specific docking score prediction, and drug–drug interactions. The use of such a method allows for state-of-the-art virtual screening with a considerable acceleration advantage of up to two orders of magnitude. The minimization of a graph variational encoder’s latent space also allows for accelerated development of specific drugs for targets with Pareto optimality principles considered, and has the added advantage of explainability.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)This work is supported by the Singapore Ministry of Education (MOE), tier 1 grants RG27/21 and RG97/22 (M.Y.). H.L.Y.I. is also supported by funding from the Agency for Science, Technology and Research (A*STAR), and A*STAR BMRC EDB IAF-PP grants (H17/01/a0/004, Skin Research Institute of Singapore; H18/01a0/016 and H22J1a0040, Asian Skin Microbiome Program)