Target 2035-update on the quest for a probe for every protein

Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. The challenge of translating the wealth of new knowledge from genomics into new medicines is that proteins, and not genes, are the primary executers of biological function. Therefore, much of how biology works in health and disease must be understood through the lens of protein function. Accordingly, a subset of human proteins has been at the heart of research interests of scientists over the centuries, and we have accumulated varying degrees of knowledge about approximately 65% of the human proteome. Nevertheless, a large proportion of proteins in the human proteome (∼35%) remains uncharacterized, and less than 5% of the human proteome has been successfully targeted for drug discovery. This highlights the profound disconnect between our abilities to obtain genetic information and subsequent development of effective medicines. Target 2035 is an international federation of biomedical scientists from the public and private sectors, which aims to address this gap by developing and applying new technologies to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome

Donated chemical probes for open science.

Potent, selective and broadly characterized small molecule modulators of protein function (chemical probes) are powerful research reagents. The pharmaceutical industry has generated many high-quality chemical probes and several of these have been made available to academia. However, probe-associated data and control compounds, such as inactive structurally related molecules and their associated data, are generally not accessible. The lack of data and guidance makes it difficult for researchers to decide which chemical tools to choose. Several pharmaceutical companies (AbbVie, Bayer, Boehringer Ingelheim, Janssen, MSD, Pfizer, and Takeda) have therefore entered into a pre-competitive collaboration to make available a large number of innovative high-quality probes, including all probe-associated data, control compounds and recommendations on use (https://openscienceprobes.sgc-frankfurt.de/). Here we describe the chemical tools and target-related knowledge that have been made available, and encourage others to join the project

Report of the National Institutes of Health SARS-CoV-2 Antiviral Therapeutics Summit

The NIH Virtual SARS-CoV-2 Antiviral Summit, held on 6 November 2020, was organized to provide an overview on the status and challenges in developing antiviral therapeutics for coronavirus disease 2019 (COVID-19), including combinations of antivirals. Scientific experts from the public and private sectors convened virtually during a live videocast to discuss severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) targets for drug discovery as well as the preclinical tools needed to develop and evaluate effective small-molecule antivirals. The goals of the Summit were to review the current state of the science, identify unmet research needs, share insights and lessons learned from treating other infectious diseases, identify opportunities for public-private partnerships, and assist the research community in designing and developing antiviral therapeutics. This report includes an overview of therapeutic approaches, individual panel summaries, and a summary of the discussions and perspectives on the challenges ahead for antiviral development

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding

Computational approaches in drug discovery and development hold great promise, with artificial intelligence methods undergoing widespread contemporary use, but the experimental validation of these new approaches is frequently inadequate. We are initiating Critical Assessment of Computational Hit-finding Experiments (CACHE) as a public benchmarking project that aims to accelerate the development of small molecule hit-finding algorithms by competitive assessment. Compounds will be identified by participants using a wide range of computational methods for dozens of protein targets selected for different types of prediction scenarios, as well as for their potential biological or pharmaceutical relevance. Community-generated predictions will be tested centrally and rigorously in an experimental hub(s), and all data, including the chemical structures of experimentally tested compounds, will be made publicly available without restrictions. The ability of a range of computational approaches to find novel compounds will be evaluated, compared, and published. The overarching goal of CACHE is to accelerate the development of computational chemistry methods by providing rapid and unbiased feedback to those developing methods, with an ancillary and valuable benefit of identifying new compound-protein binding pairs for biologically interesting targets. The initiative builds on the power of crowd sourcing and expands the open science paradigm for drug discovery

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public–private partnership benchmarking initiative to enable the development of computational methods for hit-finding

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property–free knowledge base for future anticoronavirus drug discovery