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Research Class: Accelerating Drug Discovery and Development with AI and ML Techniques

Datum i mjesto održavanja: 23. ožujka 2023. godine u 11:30 sati, u učionici O-357
Predavač: Patrik Nikolić, mag. med. chem., BioRX Partneri d.o.o.
Naziv predavanja: Accelerating Drug Discovery and Development with AI and ML Techniques
Jezik predavanja: Engleski jezik

Sažetak predavanja:

The talk will offer a comprehensive overview of AI and ML applications across various aspects of drug development, such as structural biology, high-throughput virtual screenings, molecular dynamics simulations, QSAR analysis, and the optimization of hit-to-lead processes.

Within structural biology, AI and ML are employed to forecast protein structures, pinpoint binding sites, and examine protein-ligand interactions. In high-throughput virtual screening initiatives, AI and ML are utilized to discover novel compounds with potential therapeutic effects from extensive compound libraries. In molecular dynamics simulations, AI and ML are applied to refine simulation parameters, resulting in an enhanced comprehension of small molecule properties. During QSAR analysis, AI and ML are harnessed to assess the structure-activity relationships of compounds, facilitating the identification of patterns within data that can inform the optimization process. In hit-to-lead procedures, AI and ML contribute to the enhancement of new compound properties, increasing their potency, selectivity, and pharmacokinetic profile.

The presentation will wrap up with an intriguing question: Can AI and ML supplant pharmaceutical chemists and drug hunters in the drug discovery and development process? The answer might astonish you! I invite you to attend this captivating talk to gain insight into the role of AI and ML in drug discovery and development, and to discover whether these technologies will revolutionize the future of medicinal chemistry.

Životopis predavača:

Patrik Nikolić is the CEO of BioRX Partners LLC, drug discovery consulting company. His scientific research and interest involve drug discovery using computational chemistry with emphasis on high-throughput virtual screening protocols and molecular dynamics simulations, drug development, and translational research.

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