Molecule AI

🌟 Exciting News! 🌟 We’re thrilled to announce that 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐀𝐈 has been recognized as an 𝐄𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐀𝐈 𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 at the BioSpectrum Asia Excellence Awards 2024! 🎉This honor reflects our commitment to innovation in the life sciences sector. https://lnkd.in/gakMzQXv A big thank you to BioSpectrum Asia for this recognition, and we also extend our warmest congratulations to all the other exceptional winners across various categories: Eppendorf , Cytiva , West Pharmaceutical Services, Catalent Pharma Solutions, Medidata Solutions GenScript, Ardor Biomed India PVT LTD, Oliver Healthcare Packaging, and Asia Pacific Medical Technology Association (APACMed). Together, we’re advancing the life sciences field and building a sustainable bioeconomy across Asia. 🌏💼 See you at the awards in Singapore on December 6th for a night of celebration and inspiration! #BioSpectrumAwards #MoleculeAI #LifeSciences #Innovation #Biomanufacturing #SustainableBioeconomy

𝐈𝐬 𝐀𝐈-𝐠𝐮𝐢𝐝𝐞𝐝 𝐛𝐢𝐨𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐚𝐫𝐨𝐮𝐧𝐝 𝐭𝐡𝐞 𝐜𝐨𝐫𝐧𝐞𝐫? 🌏✨ Explore the answer to this with Molecule AI, in our October edition of the newsletter! The APAC region is leading the way with cutting-edge facilities and innovative strategies. From AI-driven efficiencies to sustainable practices, discover how nations like India, China, and Singapore are transforming the production of lifesaving biologics and therapies. Don’t miss the insights into the trends and technologies shaping the next era of healthcare! 🚀🔬 #Biomanufacturing #Innovation #MoleculeAI

𝐖𝐞𝐥𝐜𝐨𝐦𝐞 𝐭𝐨 "𝐓𝐡𝐞 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐀𝐈 𝐎𝐝𝐲𝐬𝐬𝐞𝐲" 𝑰𝒏 𝒕𝒉𝒆 𝒑𝒓𝒆𝒗𝒊𝒐𝒖𝒔 𝒑𝒐𝒔𝒕 𝒘𝒆 𝒍𝒆𝒂𝒓𝒏𝒕 𝒂𝒃𝒐𝒖𝒕 𝒉𝒐𝒘 𝑴𝑨𝑰-𝑳𝒊𝒈 𝑻𝒂𝒓𝒈 𝒔𝒐𝒍𝒗𝒆𝒔 𝒕𝒉𝒆 𝒑𝒓𝒐𝒃𝒍𝒆𝒎 𝒐𝒇 𝒐𝒇𝒇 𝒕𝒂𝒓𝒈𝒆𝒕 𝒕𝒐𝒙𝒊𝒄𝒊𝒕𝒚: Molecule GEN’s 𝐋𝐢𝐠-𝐓𝐚𝐫𝐠 module predicts 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐚𝐥𝐭𝐞𝐫𝐧𝐚𝐭𝐞 𝐭𝐚𝐫𝐠𝐞𝐭𝐬 𝐨𝐟 𝐬𝐦𝐚𝐥𝐥-𝐦𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐝𝐫𝐮𝐠𝐬. It searches a 𝐜𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐡𝐮𝐦𝐚𝐧 𝐩𝐫𝐨𝐭𝐞𝐨𝐦𝐞 and identifies 𝐩𝐫𝐨𝐭𝐞𝐢𝐧 𝐜𝐚𝐯𝐢𝐭𝐢𝐞𝐬 𝐫𝐞𝐬𝐞𝐦𝐛𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝐭𝐚𝐫𝐠𝐞𝐭 𝐩𝐫𝐨𝐭𝐞𝐢𝐧'𝐬 𝐛𝐢𝐧𝐝𝐢𝐧𝐠 𝐜𝐚𝐯𝐢𝐭𝐲. While computing the similarity measure, we consider both the 𝐬𝐡𝐚𝐩𝐞 𝐚𝐧𝐝 𝐜𝐡𝐞𝐦𝐢𝐜𝐚𝐥 𝐧𝐚𝐭𝐮𝐫𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐛𝐢𝐧𝐝𝐢𝐧𝐠 𝐜𝐚𝐯𝐢𝐭𝐲. The model also provides the binding affinity for the ligand against those proteins that it estimates more likely to bind against. 𝐓𝐡𝐢𝐬 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐢𝐧𝐭𝐨 𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐚𝐥𝐭𝐞𝐫𝐧𝐚𝐭𝐞-𝐭𝐚𝐫𝐠𝐞𝐭 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬, aiding, for example, in the experimental validation and optimisation of lead compounds to minimise off-target interactions. 𝑰𝒏 𝒕𝒉𝒊𝒔 𝒑𝒐𝒔𝒕 𝒘𝒆 𝒘𝒊𝒍𝒍 𝒍𝒆𝒂𝒓𝒏 𝒂𝒃𝒐𝒖𝒕 𝒂 𝒄𝒂𝒔𝒆 𝒔𝒕𝒖𝒅𝒚 𝒊𝒏𝒗𝒐𝒍𝒗𝒊𝒏𝒈 𝒕𝒉𝒆 𝒃𝒆𝒔𝒑𝒐𝒌𝒆 𝒂𝒏𝒂𝒍𝒐𝒈𝒖𝒆𝒔 𝒐𝒇 𝒂 𝒍𝒆𝒂𝒅𝒊𝒏𝒈 𝒂𝒏𝒂𝒆𝒔𝒕𝒉𝒆𝒕𝒊𝒄 𝒎𝒐𝒍𝒆𝒄𝒖𝒍𝒆. 𝐊𝐞𝐭𝐚𝐦𝐢𝐧𝐞 is widely used in medical settings due to its anaesthetic, analgesic, and antidepressant properties. However, it has 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐨𝐠𝐞𝐧𝐢𝐜 𝐚𝐧𝐝 𝐨𝐭𝐡𝐞𝐫 𝐩𝐬𝐲𝐜𝐡𝐢𝐚𝐭𝐫𝐢𝐜 𝐬𝐢𝐝𝐞 𝐞𝐟𝐟𝐞𝐜𝐭𝐬, which have led to its abuse as a recreational drug. A substitute for ketamine that preserves its medical effects, while 𝐦𝐢𝐧𝐢𝐦𝐢𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐮𝐧𝐝𝐞𝐬𝐢𝐫𝐚𝐛𝐥𝐞 𝐬𝐢𝐝𝐞 𝐞𝐟𝐟𝐞𝐜𝐭𝐬 would represent a significant medical advancement with a very large market demand. One of the major targets of ketamine action is believed to be the 𝐍-𝐦𝐞𝐭𝐡𝐲𝐥 𝐃-𝐚𝐬𝐩𝐚𝐫𝐭𝐚𝐭𝐞 𝐫𝐞𝐜𝐞𝐩𝐭𝐨𝐫 (𝐍𝐌𝐃𝐀𝐑). We have used our 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐆𝐄𝐍 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 for the de novo design of 𝐍𝐌𝐃𝐀𝐑 𝐛𝐢𝐧𝐝𝐞𝐫𝐬 with druglike properties and a safe 𝐀𝐃𝐌𝐄𝐓 𝐩𝐫𝐨𝐟𝐢𝐥𝐞. A clinical research team in Waikato, New Zealand, is testing our designed molecules through brain-slice experiments on rodents. Below are some properties of representative 𝐍𝐌𝐃𝐀𝐑 binder molecules generated by us, along with those for ketamine as the reference molecule. 𝐖𝐢𝐭𝐡 𝐭𝐡𝐢𝐬 𝐰𝐞 𝐰𝐫𝐚𝐩 𝐮𝐩 𝐭𝐡𝐞 𝐒𝐞𝐚𝐬𝐨𝐧 1 𝐨𝐟 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐀𝐈 𝐎𝐝𝐲𝐬𝐬𝐞𝐲: 𝐀 𝐣𝐨𝐮𝐫𝐧𝐞𝐲 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧, 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡𝐬, 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐝𝐫𝐮𝐠 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲. 𝐒𝐭𝐚𝐲 𝐭𝐮𝐧𝐞𝐝 𝐚𝐬 𝐰𝐞 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐞 𝐭𝐨 𝐩𝐮𝐬𝐡 𝐛𝐨𝐮𝐧𝐝𝐚𝐫𝐢𝐞𝐬 𝐢𝐧 𝐒𝐞𝐚𝐬𝐨𝐧 2! #MoleculeAIOdyssey #AIDrugDiscovery #InnovatingTheFuture"

🙏 A Heartfelt Thank You from Molecule AI! 🌿 It's been a week since Global Bio India 2024, and we’re still buzzing with excitement from the incredible experience at the event. We want to extend our deepest thanks to everyone who visited us, engaged in discussions, and showed such genuine interest in our AI-driven platform, MoleculeGEN. Your enthusiasm and curiosity about how AI can revolutionize drug discovery truly made the event unforgettable. A special shoutout to the organizers of Global Bio India 2024: Biotechnology Industry Research Assistance Council (BIRAC) and Department of Biotechnology, for creating such an impactful platform for innovation, collaboration, and knowledge-sharing. The event provided us with the opportunity to connect with brilliant minds and share our vision for the future of healthcare. We’re excited to continue building on the momentum of the event and look forward to future collaborations and breakthroughs! #GlobalBioIndia2024 #Moleculeai #ThankYou #AIinBiotech #DrugDiscovery #InnovationInHealthcare #FutureOfHealth #ai #pharmacy #machinelearning

🌟 Welcome to Day 2 of Global Bio India 2024! 🌟 We’re back for Day 2 at POD no. 161, Hall no. 5, Pragati Maidan, New Delhi, and ready for another exciting day of biotech innovation! 🎯 Visiting Hours: 3:30 to 6 PM 👥 Who’s here? Come meet Mr. Vishay Rawar and Dr. Sameer Agarwal, PhD, representing Molecule ai, and learn about our game-changing AI platform, MoleculeGEN. 💡 Whether you’re curious about how AI is transforming drug discovery or want to see how MoleculeGEN can accelerate R&D, Day 2 is your chance to dive deep into the future of healthcare. Let’s continue exploring biotech breakthroughs together! #GlobalBioIndia2024 #Moleculeai #Day2 #AIinBiotech #DrugDiscovery #InnovationInHealthcare #FutureOfHealth #AI #pharmacy #computatuionalchemistry

🌟 Day 1 Highlights from Global Bio India 2024 🌟 Day 1 at Global Bio-India 2024 was a great success! We had an enthusiastic crowd visiting us at POD no. 161, eager to learn more about our AI-powered platform, MoleculeGEN. 💡 Key Takeaways: Attendees were particularly interested in how MoleculeGEN works and its potential to transform the drug discovery process. We discussed the platform’s ability to: Accelerate drug discovery by predicting new molecular structures faster than traditional methods. Reduce costs through highly efficient computational models. Improve accuracy in identifying viable drug candidates with enhanced AI-driven data analysis. 👥 Visitors were excited about the prospect of MoleculeGEN revolutionizing the pharmaceutical industry by reducing timelines, improving the success rate of new therapies, and ultimately reshaping healthcare innovation. As Day 1 comes to a close, we look forward to continuing these discussions and sharing more insights with industry experts and innovators. #MoleculeGEN #AIinHealthcare #DrugDiscovery #InnovationInBiotech #GlobalBioIndia2024

🌟 Day 1 Continues at Global Bio India 2024! 🌟 The program is in full swing, and now it’s time to visit us at POD no. 161, Hall no. 5, Pragati Maidan, New Delhi! 🎯 Visiting hours: 3:30 to 6 PM 👥 Who’s here? Come meet Mr. Vishay Rawar and Dr. Sameer Agarwal, PhD, representing Molecule AI, and learn about our groundbreaking work in AI-driven drug discovery. Get an exclusive look at 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞𝐆𝐄𝐍, 𝐨𝐮𝐫 𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐭𝐡𝐚𝐭'𝐬 𝐫𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐝𝐫𝐮𝐠 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐛𝐢𝐨𝐭𝐞𝐜𝐡 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧. Don’t miss your chance to connect with us today and explore how we’re advancing the future of health! #GlobalBioIndia2024 #Moleculeai #AIinBiotech #DrugDiscovery #InnovationInHealthcare #Day1 #computationalchemistry #ai

Join us on 𝐒𝐞𝐩𝐭𝐞𝐦𝐛𝐞𝐫 12-13-14, at 𝐁𝐨𝐨𝐭𝐡 𝐧𝐨. 161, 𝐇𝐚𝐥𝐥 𝐧𝐨. 5, 𝐏𝐫𝐚𝐠𝐚𝐭𝐢 𝐌𝐚𝐢𝐝𝐚𝐧, New Delhi at Global Bio India 2024 organized by Biotechnology Industry Research Assistance Council (BIRAC) and Department of Biotechnology, where global stakeholders gather to showcase innovations in biotechnology. 💡 Discover 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞𝐆𝐄𝐍, our cutting-edge AI-powered platform revolutionizing drug discovery. Learn how AI is transforming the 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐨𝐟 𝐧𝐞𝐰 𝐝𝐫𝐮𝐠𝐬 and shaping the future of healthcare. Whether you're a biotech innovator, researcher, or investor, connect with us and explore the next wave of biotech breakthroughs. 🔬 𝐃𝐨𝐧’𝐭 𝐦𝐢𝐬𝐬 𝐨𝐮𝐭—𝐥𝐞𝐭’𝐬 𝐛𝐮𝐢𝐥𝐝 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐡𝐞𝐚𝐥𝐭𝐡, 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫!" #ai #drugdiscovery #computationalchemistry #machinelearning #globalevent #governmentofindia #biotechnology

In the September edition of 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐀𝐈 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫, we explore groundbreaking advancements in the fight against antimicrobial resistance (AMR) through AI-driven research. Institutions like Stanford University School of Medicine and McMaster University have introduced 𝐒𝐲𝐧𝐭𝐡𝐞𝐌𝐨𝐥, 𝐚𝐧 𝐀𝐈 𝐦𝐨𝐝𝐞𝐥 that creates novel drugs targeting resistant strains like Acinetobacter baumannii. Meanwhile, Massachusetts Institute of Technology and McMaster University have discovered a new antibiotic using AI that combats drug-resistant infections. Broad Institute of MIT and Harvard is utilizing AI to develop compounds to neutralize stubborn pathogens such as methicillin-resistant Staphylococcus aureus (MRSA). The University of Manitoba is employing explainable AI (XAI) to create antibiotics with fewer side effects. Oxford Brookes University has developed an AI method to detect antibiotic resistance in just 30 minutes, and Eli Lilly, in collaboration with OpenAI, is leveraging generative AI to discover novel antimicrobials to tackle resistant pathogens. These cutting-edge innovations show how AI is reshaping drug discovery and combating AMR globally.

𝐖𝐞𝐥𝐜𝐨𝐦𝐞 𝐭𝐨 "𝐓𝐡𝐞 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐀𝐈 𝐎𝐝𝐲𝐬𝐬𝐞𝐲" 𝑰𝒏 𝒕𝒉𝒆 𝒑𝒓𝒆𝒗𝒊𝒐𝒖𝒔 𝒑𝒐𝒔𝒕 𝒘𝒆 𝒍𝒆𝒂𝒓𝒏𝒕 𝒂𝒃𝒐𝒖𝒕 𝒐𝒖𝒓 4𝒕𝒉 𝒎𝒐𝒅𝒖𝒍𝒆 𝑴𝑨𝑰 𝑳𝒊𝒈-𝑻𝒂𝒓𝒈 𝒂𝒏𝒅 𝒕𝒉𝒆 𝒑𝒓𝒐𝒃𝒍𝒆𝒎 𝒊𝒕 𝒔𝒐𝒍𝒗𝒆𝒔. A BRIEF SUMMARY ABOUT OUR PREVIOUS POST: 𝐎𝐟𝐟-𝐭𝐚𝐫𝐠𝐞𝐭 𝐭𝐨𝐱𝐢𝐜𝐢𝐭𝐲, a significant risk associated with small-molecule drugs, often leads to adverse side effects and ranks as a primary reason for the failure of clinical trials (https://lnkd.in/dxGpyU6U). Highly promiscuous compounds are generally undesirable because of their high toxicity. For this reason, efforts are directed at developing compounds that hit only 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 targets. This task is 𝐩𝐚𝐫𝐭𝐢𝐜𝐮𝐥𝐚𝐫𝐥𝐲 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐢𝐧𝐠 for protein families such as kinases, proteases, and ligases because the binding sites of members of the same family are 𝐡𝐢𝐠𝐡𝐥𝐲 𝐬𝐢𝐦𝐢𝐥𝐚𝐫 (https://lnkd.in/d3DccXKj). The 𝐛𝐢𝐧𝐝𝐢𝐧𝐠 𝐬𝐢𝐭𝐞 𝐬𝐢𝐦𝐢𝐥𝐚𝐫𝐢𝐭𝐲 is also a critical factor in detecting 𝐨𝐟𝐟-𝐭𝐚𝐫𝐠𝐞𝐭 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬, with a well-established correlation linking pocket similarity to ligand promiscuity (https://lnkd.in/dG-ZDVfw). At the same time, molecules binding to more than one target are, by definition, necessary for the success of drug repurposing, and binding site similarity is a crucial factor from this perspective as well (https://lnkd.in/dqzdTQiU). 𝑰𝒏 𝒕𝒉𝒊𝒔 𝒑𝒐𝒔𝒕 𝒘𝒆 𝒘𝒊𝒍𝒍 𝒍𝒆𝒂𝒓𝒏 𝒂𝒃𝒐𝒖𝒕 𝒉𝒐𝒘 𝑴𝑨𝑰-𝑳𝒊𝒈 𝑻𝒂𝒓𝒈 𝒔𝒐𝒍𝒗𝒆𝒔 𝒕𝒉𝒆 𝒂𝒃𝒐𝒗𝒆 𝒑𝒓𝒐𝒃𝒍𝒆𝒎𝒔 𝒘𝒊𝒕𝒉 𝒂𝒏 𝒊𝒏𝒇𝒆𝒓𝒆𝒏𝒄𝒆: Molecule GEN’s 𝐋𝐢𝐠-𝐓𝐚𝐫𝐠 module predicts 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐚𝐥𝐭𝐞𝐫𝐧𝐚𝐭𝐞 𝐭𝐚𝐫𝐠𝐞𝐭𝐬 𝐨𝐟 𝐬𝐦𝐚𝐥𝐥-𝐦𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐝𝐫𝐮𝐠𝐬. It searches a 𝐜𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐡𝐮𝐦𝐚𝐧 𝐩𝐫𝐨𝐭𝐞𝐨𝐦𝐞 and identifies 𝐩𝐫𝐨𝐭𝐞𝐢𝐧 𝐜𝐚𝐯𝐢𝐭𝐢𝐞𝐬 𝐫𝐞𝐬𝐞𝐦𝐛𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝐭𝐚𝐫𝐠𝐞𝐭 𝐩𝐫𝐨𝐭𝐞𝐢𝐧'𝐬 𝐛𝐢𝐧𝐝𝐢𝐧𝐠 𝐜𝐚𝐯𝐢𝐭𝐲. While computing the similarity measure, we consider both the 𝐬𝐡𝐚𝐩𝐞 𝐚𝐧𝐝 𝐜𝐡𝐞𝐦𝐢𝐜𝐚𝐥 𝐧𝐚𝐭𝐮𝐫𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐛𝐢𝐧𝐝𝐢𝐧𝐠 𝐜𝐚𝐯𝐢𝐭𝐲. The model also provides the binding affinity for the ligand against those proteins that it estimates more likely to bind against. 𝐓𝐡𝐢𝐬 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐢𝐧𝐭𝐨 𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐚𝐥𝐭𝐞𝐫𝐧𝐚𝐭𝐞-𝐭𝐚𝐫𝐠𝐞𝐭 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬, aiding, for example, in the experimental validation and optimisation of lead compounds to minimise off-target interactions. #moleculeai #drugdiscovery #ai #medicinalchemistry #computationalchemistry #pharmacy #aimodules