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DTSTART:19700308T020000
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DTSTAMP:20210402T160552Z
LOCATION:Track 3
DTSTART;TZID=America/New_York:20201113T114500
DTEND;TZID=America/New_York:20201113T120000
UID:submissions.supercomputing.org_SC20_sess222_ws_cafcw128@linklings.com
SUMMARY:Scalable Human Pharmacokinetics Property Prediction for Cancer Dru
 g Discovery at ATOM
DESCRIPTION:Workshop\n\nScalable Human Pharmacokinetics Property Predictio
 n for Cancer Drug Discovery at ATOM\n\nMadej, Murad, Pasikanti, Minnich, M
 cComas...\n\nThe drug discovery process has been described as a large-scal
 e multi-parameter optimization problem to find new chemicals to treat dise
 ases. Not only must a new compound show efficacy to improve a disease stat
 e, a compound must also fit numerous criteria to become a new drug. A comp
 ound’s pharmacokinetics (PK) and toxicity properties are equally important
  and are even more difficult to predict. Many compounds in pre-clinical an
 d clinical trials are discontinued due to toxic effects in animals and hum
 ans. \n\nGiven the considerable time and money investments in drug discove
 ry projects, it is crucial to address PK and toxicity problems as soon as 
 possible in the discovery pipeline. The Accelerating Therapeutics for Oppo
 rtunities in Medicine (ATOM) consortium strives to tackle these problems i
 n the drug discovery process by combining cancer informatics and high-perf
 ormance computing approaches [1]. ATOM has developed a range of PK machine
  learning (ML) models to predict PK and toxicity properties early in compo
 und development [2]. Through robust ML and mechanistic PK models, ATOM aim
 s to provide early warning for PK and toxicity problems.\n\nRegistration C
 ategory: Workshop Reg Pass
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