Enabling development of accessible & equitable health care products via implementation of innovative strategies |Public Health Professional |Health for all | PhD| AMR | EIDs| Translational Research | Product development
An innovative approach for accelerating Drug discovery applying Chemoproteomics & Machine learning (ML)!!
🧩 The Missing Puzzle Pieces: Most of the proteins in our bodies lack small-molecule partners, making it hard to understand how they work or find treatments for diseases.
➡️ Application of a novel Search Strategy: A method was developed to find these missing partners by testing hundreds of small molecules and developing predictive models using ML classifiers (computer based) to predict how they interact with proteins.
💡Findings: Thousands of interactions between small molecules & proteins were found, including some promising candidates for new treatments targeting specific proteins involved in diseases.
🧑🔬 ML Framework: ML models were developed to predict fragment interactions with native proteins on a proteome-wide scale, including quantitative & qualitative interactome signatures. These models based on user-defined input of target proteins enable prediction of fragment binding to custom protein sets… Or simply put they could predict how these molecules interact with proteins.
❓Impact: The work helps us understand how proteins work & enables accelerating drug discovery efforts.
Screening reveals thousands of 'undrugged, yet druggable' proteins
Collaboration involving Pfizer develops freely accessible AI tool to probe protein–compound interactions
Screening around 400 compounds directly in cells, unearthed around 2300 proteins that are bound including a large fraction of targets for which there are no prior known ligands.
https://lnkd.in/ggpB88qX
It was an honor to participate in the insightful "Artificial Intelligence in Drug Development" event. A big thank you to Leon 'Jun' T. and Shijing (Nora) Luo, for hosting such a fantastic event. The panel discussion delved into AI’s transformative role in drug development, with expert contributions from Alexandra Snyder, MD, Lipeng Lai, and Lily Zhang, each sharing unique perspectives on AI's profound impact on the pharmaceutical industry. Kudos to the InScienceWeTrust Community, SAPA-GP (Sino-American Pharmaceutical Professionals Association -Greater Philadelphia), and BioSpark Group for organizing this event!
During the event, our Co-Founder and CEO, Sharon Chen, highlighted how GenAI-powered solutions are set to revolutionize clinical development by optimizing both data management and medical writing across every stage. Some key takeaways from Sharon's sharing:
According to a recent McKinsey report, GenAI has the potential to generate $13 to $25 billion annually in clinical development, creating substantial value for the industry. At AlphaLife Sciences, we build end-to-end, intelligent, modular solutions tailored to the unmet needs of sponsors and CROs. These highly adaptable solutions enhance clinical trial efficiency. Our offerings include:
* GenAI-Copiloted Medical Writing Solutions: These solutions accelerate document creation while improving process efficiency, quality, and data consistency. Key applications include CSR writing, patient safety narratives, protocol writing, protocol design, and more.
* GenAI-Powered Data Management Solutions: These solutions streamline data management, improve decision-making, ensure data integrity, and support regulatory compliance. Key applications include EDC, data monitoring, and more.
At AlphaLife Sciences, we believe that GenAI transcends process efficiency—it unlocks insight efficiency and concept efficiency, empowering AI to extract transformative insights that drive innovation in clinical development.
For more information, visit https://lnkd.in/epRVtrve#ArtificialIntelligence#MedicalWriting#DataManagement#ClinicalTrials#AlphaLifeSciences#GenAI#AlphaLifeSciences#clinicalresearch#GenAIsoftware#GenAISolution
https://lnkd.in/eJMbkuU4
⏰⏰⏰ Our popular #BioverseWebinar Ep 12 #ArtificialIntelligenceInDrugDevelopment is now on YouTube! Thank you, Alexandra Snyder, MD from Generate:Biomedicines, Lily Zhang from Jones Day, Sharon Chen from AlphaLife Sciences, and Lipeng Lai from XtalPi Inc., for a 90-minute worth of great insights!
The panel discussion on AI's role in drug development showcased the diverse expertise of each speaker. Below are some highlights from the 90-minute webinar.
Alexandra Snyder, MD highlighted AI's potential in #BiologicsDrugDiscovery, focusing on designing antibodies with non-immunogenic targets and optimizing multiple attributes simultaneously. She also discussed the importance of collaboration and raised concerns about the ethical and social implications of AI, particularly its "black box" nature in a highly regulated environment.
Lily Zhang emphasized the significance of #IPProtection, exploring the legal and ethical challenges in AI-driven drug development. She addressed the complexities of patenting AI-generated innovations and the advantages and limitations of #Copyrights, #Patents, and #TradeSecrets in this context.
Sharon Chen concentrated on AI's impact on #ClinicalDevelopment, advocating for the use of AI to streamline processes and improve #DataManagement. She also touched on collaboration, stressing the need for AI to work within a broader ecosystem to maximize its potential in drug development.
Lipeng Lai focused on AI's applications in both #SmallMolecule and #Biologics drug development, detailing AI's success in #MolecularDesign, #ClinicalDataAnalysis, and #MarketStrategies. He also emphasized the importance of collaborations, particularly between AI companies and big pharma, to enhance efficiency and reduce costs.
Each speaker provided unique insights, reflecting their areas of expertise and the multifaceted role of AI in the pharmaceutical industry.
✨️AI's most significant impact in pharma is in drug discovery》
□ as it accelerates the identification of potential drug candidates and optimizes molecular design. By analyzing biological data.
□AI helps in predicting drug efficacy and safety profiles, shortening the time from laboratory to market.
#ai#innovation#quality#assay#purity#efficacy#safety#drug#laboratory#data#market#design#industry
Did you miss our webinar? More than 100 participants joined to learn how analytical data can be used across projects to ensure process consistency.
Mass spectrometry (LC-MS) has become a revolutionizing analytical tool for Host Cell Protein (HCP) characterization during biologics development. However, the complexity of LC-MS analysis presents significant challenges.
At Alphalyse, we have developed a method using intact standard proteins for normalization and quality control, allowing us to control the variability of our mass spectrometers.
A key element is our extensive database, the world's largest collection of HCP MS data, which allows us to fine-tune our instruments and provide our clients with exceptionally robust and reproducible analyses.
In our webinar last week, my colleague Victor Chrone showed how our AI (machine learning)-based QC tool, developed from data we have collected across 500+ HCP projects, enables us to provide consistent, accurate, and – most importantly – comparable results throughout the lifecycle of biological drugs.
Did you miss the webinar? You can access the recorded video here: https://lnkd.in/dfeDss9t
https://lnkd.in/eJMbkuU4
⏰⏰⏰ Our popular #BioverseWebinar Ep 12 #ArtificialIntelligenceInDrugDevelopment is now on YouTube! Thank you, Alexandra Snyder, MD from Generate:Biomedicines, Lily Zhang from Jones Day, Sharon Chen from AlphaLife Sciences, and Lipeng Lai from XtalPi Inc., for a 90-minute worth of great insights!
The panel discussion on AI's role in drug development showcased the diverse expertise of each speaker. Below are some highlights from the 90-minute webinar.
Alexandra Snyder, MD highlighted AI's potential in #BiologicsDrugDiscovery, focusing on designing antibodies with non-immunogenic targets and optimizing multiple attributes simultaneously. She also discussed the importance of collaboration and raised concerns about the ethical and social implications of AI, particularly its "black box" nature in a highly regulated environment.
Lily Zhang emphasized the significance of #IPProtection, exploring the legal and ethical challenges in AI-driven drug development. She addressed the complexities of patenting AI-generated innovations and the advantages and limitations of #Copyrights, #Patents, and #TradeSecrets in this context.
Sharon Chen concentrated on AI's impact on #ClinicalDevelopment, advocating for the use of AI to streamline processes and improve #DataManagement. She also touched on collaboration, stressing the need for AI to work within a broader ecosystem to maximize its potential in drug development.
Lipeng Lai focused on AI's applications in both #SmallMolecule and #Biologics drug development, detailing AI's success in #MolecularDesign, #ClinicalDataAnalysis, and #MarketStrategies. He also emphasized the importance of collaborations, particularly between AI companies and big pharma, to enhance efficiency and reduce costs.
Each speaker provided unique insights, reflecting their areas of expertise and the multifaceted role of AI in the pharmaceutical industry.
#Peptides and #DrugDiscovery – Part 5
In our previous post, we discussed how high-throughput screening expanded the peptide drug market with many new approvals. Yet, the discovery process remained lengthy, costly, and risky for pharma companies. Enter #AI—possibly the field's greatest revolution to date!
Artificial intelligence and machine learning can now analyze vast datasets of peptide candidates, predict their stability, efficacy, and interactions with biological targets, and manage large volumes of data to find superior peptide drug candidates.
All of this is done at a phenomenal rate, turning years of drug discovery into days—at a fraction of the cost!
This is exactly what we are doing at Pepticom >> https://pepticom.com/
Read our previous post here > https://lnkd.in/dqsz77c7#healthcareinnovation
The era of causal AI and digital twins is set to change drug discovery - read more about how Aitia is currently using this technology to uncover new therapeutic approaches:
This webinar will be comparing purification efficiency between filter plates, magnetic beads and packed tips/columns.
Will be a great resource for those in #ProteinSciences, Upstream/Downstream #BioProcessing, #Bioanalytical, #AntibodyEngineering, or those who are interested in high-throughput antibody purification.
Automation in early antibody and protein drug development converts traditional purification into high-throughput processes, enabling better exploration of protein expression and stability before scaling up to large volumes.
Join our webinar for expert tips on selecting the right automation platform and discover how it can enhance your screening and process development. Don’t miss out!
https://hubs.ly/Q02QpN1N0
Automation in early antibody and protein drug development converts traditional purification into high-throughput processes, enabling better exploration of protein expression and stability before scaling up to large volumes.
Join our webinar for expert tips on selecting the right automation platform and discover how it can enhance your screening and process development. Don’t miss out!
https://hubs.ly/Q02QpN1N0
Aurigene.AI’s multiparameter optimization tool (MPO-ADMET) plays a significant role in early drug discovery by predicting and prioritizing compounds for their potency, safety, drug-likeness, and pharmacokinetics. This iterative process helps reduce late-stage discovery failures. The predictive models are built on high-quality datasets and benchmarked against global standards.
Want to know more? Click here: https://lnkd.in/gZ5SFCVF to read our colleague Lijo John’s perspective on Aurigene.ai.