Auto articles: an experiment in AI-generated content

T5 abstract: a generative model bridges systems biology and molecular design. it can design active-like molecules for desired targets without any previous annotation of the training compounds. this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.

Pegasus abstract: we report a generative model systems and conditioning method for de novo design of active molecules with desired transcriptomic profile as the desired state. the desired state is provided by the model that is able to design active molecules for desired targets without any previous target annotation of compounds. this method represents an alternative approach to bridge chemistry and to this long road biology in difficult drug discovery.

AI² SCITLDR paper: A generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data.

SciBERT introduction: The difficulty of the drug discovery process stems from the fact that only a small fraction of the theoretically possible 1060 drug-like molecules are therapeutically relevant. One of the most challenging tasks in this scenario is the hit identification, namely the identification of small molecules with an adequate (but usually weak) activity on a specific target that could then be used as a starting point for the chemical optimization process. Although these methods generate copious bioactivity data, they are not very efficient as the amount of resources required is disproportionally large compared to the small number of hits discovered. Techniques such as virtual screening aim to identify hits from virtual libraries containing large number of molecules, usually by similarity-based searches or by molecular docking. Other generative models such as variational autoencoders (VAE) have been used to automatically optimize molecules to improve their physicochemical and drug-likeness properties. Herein, we report a generative model that bridges systems biology and molecular design. This approach presents several advantages such as the generation of hit-like molecules without the need of previous knowledge of active compounds, biological activity data, or target annotations.

SciBERT discussion: In conclusion, we reported a method based on conditional generative adversarial networks that proposes new molecules from a particular gene expression signature. Our method offers some advantages over current molecular generative approaches as well as an alternative way to exploit all the information contained in compound-induced gene expression data, in particular the one in L1000 database. Since no previous measurement of biological activity or target annotations are needed for the training data, this approach could potentially be applied to any target, as we have shown herein.

T5 abstract: drug target identification is a crucial step in development, yet is also among the most complex. we develop BANDIT, which integrates multiple data types to predict drug binding targets. from this set, we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells.

Pegasus abstract: target identification is crucial in drug development. we developed a machine-learning approach to predict drug binding targets. we benchmarked public data to 90% accuracy on 14,000 compounds without known targets. this set was used to validate 14 novel microtubule inhibitors. we applied this to 3201 anticancer compounds in clinical development.

AI² SCITLDR paper: We develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets.

SciBERT introduction: It typically takes 15 years and 2.6 billion dollars to go from a small molecule in the lab to an approved drug and for natural products and phenotypic screen derived small molecules, one of the greatest bottlenecks is identifying the targets of any candidate molecules. Traditionally, ligand-based approaches take known binding targets for a given drug and attempt to find other drugs or proteins that are sufficiently similar. However, to achieve high predictive power they require a large input of known binding partners for each tested drug, and therefore can only be used on drugs which have prior comprehensive target information. However these approaches still suffer from a few limitations: 1. To overcome these limitations, we introduce BANDIT, a drug-target prediction platform. BANDIT uses a Bayesian approach to integrate a number of diverse data types in an unbiased manner and provides a platform that allows for simple integration of new datatypes as they become available. Tested on ~2000 different compounds, BANDIT achieves a high accuracy at identifying shared target interactions, uncovers novel targets for the treatment of cancer, and can be used to quickly pinpoint potential therapeutics with novel mechanisms of action to accelerate drug development.

SciBERT discussion: We have developed BANDIT, an integrative Big-Data approach that combines a set of individually weak features into a single reliable and robust predictor of shared-target drug relationships and individual drug binding targets. Finally, BANDIT can be used on a broader scale to discern mechanisms of approved drugs, characterize the global drug universe landscape, explain existing, yet puzzling, clinical phenotypes, and repurpose drugs for new indications. However, as more information becomes available there are many aspects of the current implementation that can be improved. Furthermore, BANDIT could be used to rapidly screen a large database of compounds and efficiently identify any promising therapeutics that could be further evaluated.

T5 abstract: chemoproteomics is a key technology to characterize the mode of action of drugs. it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds.

Pegasus abstract: mass spectrometry is a key technology to characterize the mode of action of drugs as it identifies protein targets of bioactive compounds. we here develop mass- spectrometry based on limited proteolysis that works in human cells including machine learning to discern drug targets of small molecules and integrate them into a single score to identify their binding sites.

AI² SCITLDR paper: We develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry to identify drug

SciBERT introduction: Unraveling the mechanism of action and molecular target of small molecules remains a major challenge in drug development. However, these target capturing probes may perturb molecular interactions and biological functions. This is achieved through the global detection with liquid chromatography-coupled tandem mass spectrometry (LC-MS) of differential proteolytic patterns produced upon ligand binding. Here we adapt this LiP-based approach to enable the systematic investigation of protein–small-molecule interactions in complex eukaryotic proteomes (e.g. human). We benchmark LiP-Quant by investigating binding of compounds to known human protein targets, addressing the possibility of detecting kinase or phosphatase inhibitors and membrane protein drugs.

SciBERT discussion: Devising modification-free chemoproteomic strategies that simultaneously probe whole-proteomes will increase efficiency in the drug development pipeline. Although overall the TPP-based approaches examined here are more sensitive, it should be noted that true positive identification rates for LiP-Quant and TPP are virtually identical for the first 25 targets identified. LiP-Quant identifies peptides that undergo structural changes upon compound binding, providing a proxy for drug binding sites and enabling a quantitative assessment of their binding specificity and selectivity through the estimation of EC50s. For instance, we used LiP-Quant with additional validation to discover the potential targets of a research fungicide. We have found that LiP-Quant detects interactions with drugs over a broad range of affinities from nM to µM. EC50s estimated from lysate with LiP-Quant are often higher, by approximately an order of magnitude, than literature-reported values generally measured with recombinant proteins. Further applying the LiP-Quant pipeline to cells that remain intact during drug treatment represents a more biologically relevant experiment, while ensuring proteins maintain their native compartment during compound incubation. As such approaches deviate from the standard LiP-Quant pipeline, they are in their infancy and will require further development to reduce false positives/negatives but they offer a glimpse of potential areas for further development.