Data science is getting widespread in pharma and medicine. This meeting will connect data scientists, bench scientists, clinicians, entrepreneurs, industry executives, investors and patients. It is planned for winter 2020 at Topos House, San Francisco, California (details below).

Space is limited at Topos House, so participants will be carefully selected, according to their potential contribution to the event.

However, some lectures will be broadcasted live on YouTube, with interactive Q&A sessions with viewers worldwide.

The conference is divided into 2 tracks (details below):

Precision oncology

Drug discovery: deep learning and beyond

However, organization is flexible: suggestions for additional tracks are welcome, especially from under-appreciated applications of machine learning to healthcare. We are aware that some subjects can fly under the radar, despite having huge impact potential.

Non-original papers are welcome: you can submit contributions already published elsewhere.

Join speakers from Stanford, Novartis, Parker Institute… and send your pitch, or presentation proposal, to Mostapha Benhenda:

Speakers get free entry.

If you wish to attend without giving a talk, send an email to: mostaphabenhenda@gmail.com explaining your motivations (Prices are not fixed yet, but tickets will be much cheaper than JP Morgan healthcare conference, we promise)

Some limited accomodation can be possible on-site at Topos House.

Track 1: Precision oncology

Gene sequencing costs are dropping, facilitating treatment strategies based on genetic tests results.

Cancer patients are among the firsts to benefit from this evolution towards precision medicine. A new generation of treatments are emerging: targeted therapies, immunotherapies (immune checkpoint inhibitors, CAR-T, cancer vaccines…), or DNA repair inhibitors. They are prescribed alone, or in combinations with other therapies, including surgery, radiotherapy, or chemotherapy.

Possible topics include, but are not limited to:

Prediction of therapy responses, using transcriptomics biomarkers, or alternatives.

Neoantigens identification, for personalized cancer vaccines

Prediction of epigenetic modifications

Patient selection for clinical trials

Radiomics, and other applications of computer vision

Electronic health records, and other applications of natural language processing

Gene sequencing and gene editing ( CRISPR-Cas9) applications of machine learning

And more!

Track 2: Drug discovery: deep learning and beyond

Deep learning has been successful in computer vision, text processing and voice recognition, inspiring many applications to drug discovery.

This conference will:

Evaluate the impact of various deep learning methods (CNN, RNN, RL, GAN…) on real-world drug discovery pipelines.

Critically discuss the value of cutting-edge techniques, compared to older computer-aided drug design methods, developed over the last 40 years. Newer is not always better.

Explore solutions for acquiring large high-quality datasets, crucial for the performance of deep learning algorithms.

Possible topics include, but are not limited to:

De novo molecular design and optimization for small molecules, peptides, proteins, RNA…

Chemical synthesis planning (retrosynthesis and reaction prediction)

Molecular activities and properties predictions

Drug repurposing

Text mining of scientific literature with natural language processing

Lab automation

And more!

About Topos House, San Francisco

Topos House is a community of scientists dedicated to life-long learning. On 4000 square feet, Topos House hosts events with different formats (seminars, lectures, book talks, peer learning groups…). Located 5 min from BART, 40 min drive to Palo Alto, 18 min to SF Downtown, there’s ample parking nearby.

Learn more about Topos House: www.topos.house