Company: Standigm Inc.
Nomination Submitted by: Bospar PR
Company Description: Standigm is a workflow AI drug discovery company with headquarters in South Korea and subsidiaries in the USA and the UK. The company has proprietary AI platforms encompassing novel target identification to compound design, to generate commercially valuable drug pipelines. It has developed an early-stage drug discovery workflow AI to generate multiple first-in-class compounds within seven months.
Nomination Category: Company / Organization Categories
Nomination Sub Category: Company of the Year - Pharmaceuticals - Medium-size
 Nomination Title: Standigm Inc.
- Which will you submit for your nomination in this category, a video of up to five (5) minutes in length about the achievements of the nominated organization since 1 January 2020, OR written answers to the questions for this category? (Choose one):
Written answers to the questions
- If you are submitting a video of up to five (5) minutes in length, provide the URL of the nominated video here, OR attach it to your entry via the "Add Attachments, Videos, or Links to This Entry" link above, through which you may also upload a copy of your video.
- If you are providing written answers for your submission, you must provide an answer to this first question: Briefly describe the nominated organization: its history and past performance (up to 200 words):
Total 172 words used.
Standigm is a Seoul-based workflow AI drug discovery company co-founded by three drug discovery science and technology experts in 2015. The three joined forces to bridge their areas of expertise across AI, chemical engineering, and systems biology to provide better solutions for drug discovery by employing unique AI algorithms. Pursuing full-stack, AI-driven industrializing drug discovery, Standigm created an automated molecular design workflow that now covers the entire drug discovery process across interconnected Standigm AI platforms, including Standigm ASK™ for novel target discovery and Standigm BEST for a novel compound generation.
Standigm is expanding across the globe, recently opening offices in the US and the UK. This expansion has enabled Standigm to carry out multiple projects simultaneously, using its workflow AI technology to meet the needs of various novel scenarios of early drug discovery. Standigm’s ultimate goal is to use its workflow AI to complete the closed-loop system whereby any data produced in projects are fed back for continuous improvement while reducing the resources to secure patentable lead compounds inhibiting a novel target.
- If you are providing written answers for your submission, you must provide an answer to this second question: Outline the organization's achievements since the beginning of 2020 that you wish to bring to the judges' attention (up to 250 words):
Total 234 words used.
Since 2020, Standigm has operated iCLUE&ASK, an open version of its target identification platform Standigm ASK™, where researchers can directly experience its core functions in order to discover new targets. This endeavor has encouraged researchers to conduct drug discovery research in specialized fields, including rare-disease areas.
Rare diseases are severely underrepresented in drug discovery research. Some of the main reasons for this include insufficient knowledge of the disease-target association and natural history, insufficient validated outcomes, and delays associated with disease-specific biomarkers.
Idiopathic pulmonary fibrosis (IPF) is a rare disease that affects approximately five million people worldwide. The prevalence is estimated to be slightly greater in men (20.2/100,000) than in women (13.2/100,000). The mean age at presentation is 66 years. IPF initially manifests with symptoms of exercise-induced breathlessness and dry coughing. Standigm provided novel targets for IPF to our collaborator using Standigm ASK™. Pulmonary fibrosis is characterized by extracellular matrix (ECM) abnormalities, epithelial to mesenchymal transition (EMT), and the differentiation of myofibroblast derived from various cell types. To identify novel targets on IPF, three targets are selected using the ASK platform; our collaborator tested these targets and all three showed significant suppression of the EMT pathway on the knockdown. The results show the effectiveness of targets for the EMT pathway related to IPF. We are continuing the experiment for validation, specifically to show the target effect on the in vitro model.
- If you are providing written answers for your submission, you must provide an answer to this third question: Explain why the achievements you have highlighted are unique or significant. If possible compare the achievements to the performance of other players in your industry and/or to the organization's past performance (up to 250 words):
Total 241 words used.
Selecting a disease target is the first step in drug research and an important step that greatly affects the value and success of a new drug to be developed. However, the traditional approach for target discovery is through expert knowledge and manual information gathering, which limits the search to a wide knowledge space. In addition, there is a limit to the ability of humans to digest rapidly increasing literature and information. Standigm takes a different approach by applying an AI algorithm that can explore and infer the path of the graph network based on our own database, which is built to more efficiently synthesize and process the vast biological information known to date to determine the correlation between specific diseases and the proteins capable of targeting those diseases. The company has developed a methodology for scoring and integrating those proteins; based on the calculated score, Standigm can derive ranked lists of protein candidates that can target specific diseases – or disease candidates expected to be able to target a specific protein – and present not only the ranked results but an underlying path forward for each result. In order to test or set various hypotheses, Standigm provides its open, interactive iCLUE&ASK user interface environment, allowing researchers to explore and experiment. This is one of many aspects of Standigm that set the company apart from competitors. Standigm ASK’s disease-target prediction success rate is 2.6 times higher than competing platforms’ success rates.
- You have the option to answer this final question: Reference any attachments of supporting materials throughout this nomination and how they provide evidence of the claims you have made in this nomination (up to 250 words):
Total 37 words used.
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