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VERSION:2.0
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SUMMARY:Aligning Machine Learning Methods with Drug Discovery Objectives
DTSTART:20260512T151500
DTEND:20260512T161500
DTSTAMP:20260507T180114Z
UID:b31d9e94e820a8ff5408515317e70ad391cf525ec12cd3ecff87d5b7
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Paula Torren Peraire\nMachine learning is increasingly sha
 ping drug discovery\, with computational methods addressing challenges in 
 molecular design\, synthesis planning\, and optimization. In this talk\, I
  will present recent work on how computational choices influence discovery
  outcomes\, from retrosynthesis to generative chemistry. I will first exam
 ine how different combinations of single-step and multi-step retrosynthesi
 s models guide exploration of retrosynthetic space and can enable converge
 nt synthesis planning. I will then discuss de-novo molecular design\, hi
 ghlighting how multi objective optimization strategies shape molecular exp
 loration in many objective design settings. This talk underscores the impo
 rtance of aligning algorithmic decisions with practical discovery objectiv
 es and explores opportunities for integrating data driven methods into med
 icinal chemistry workflows.\n\nAbout the speaker\nPaula Torren Peraire is 
 a Data Science Innovation Fellow at Novartis in Basel\, where she works on
  machine learning driven methods for drug discovery\, with a focus on gene
 rative chemistry. She studied Pharmacy at the University of Barcelona\, fo
 llowed by a Master’s in Bioinformatics for Health Sciences at Universita
 t Pompeu Fabra. Paula received her PhD from the Technical University of Mu
 nich with a joint project between Helmholtz Munich and Johnson & Johnson\,
  focusing on computer-aided synthesis planning. Overall\, her work aims to
  develop robust computational frameworks that support molecular design and
  decision-making in drug discovery\, bridging methodological innovation wi
 th real-world pharmaceutical applications.\n \n 
LOCATION:CH G1 495
STATUS:CONFIRMED
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