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SUMMARY:How To Accelerate Molecular Insights - Efficient Distance Calculat
 ions In Python - Adam Staniszewski
DTSTART;TZID=Europe/Warsaw:20250821T160000
DTEND;TZID=Europe/Warsaw:20250821T162000
DTSTAMP:20260906T120209Z
UID:pretalx-euroscipy-2025-8U3PB3@pretalx.com
DESCRIPTION:In the rapidly evolving field of chemo- and bioinformatics\, t
 he efficient computation of molecular distances plays a crucial role in ap
 plications such as drug discovery\, molecular clustering\, and structure-a
 ctivity relationship modeling. The ability to accurately and efficiently m
 easure molecular similarity is essential for tasks ranging from virtual sc
 reening to predictive modeling. As molecular datasets continue to grow in 
 size and complexity\, scalable and computationally efficient distance metr
 ics become increasingly necessary to facilitate large-scale analysis.\n\nI
 n this work\, we explore how Python’s numerical computing capabilities c
 an be leveraged to implement a diverse range of molecular distance metrics
 . We focus on optimizing computations for vectorized molecular representat
 ions\, ensuring that performance remains competitive with highly optimized
  C++-based solutions. By utilizing efficient numerical libraries\, we demo
 nstrate that Python can achieve substantial execution speed while maintain
 ing the flexibility and ease of implementation that make it a preferred ch
 oice for many researchers.\n\nBeyond implementation\, we conduct a compreh
 ensive performance evaluation by comparing our Python-based methods agains
 t state-of-the-art libraries written in C++. Our benchmarking includes ass
 essments of computational efficiency\, memory usage\, and scalability on l
 arge molecular datasets. The results illustrate that\, with appropriate op
 timizations\, Python-based approaches can serve as
LOCATION:Room 1.20 (Ground Floor\, Shannon)
URL:https://pretalx.com/euroscipy-2025/talk/8U3PB3/
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