'Target interactions' diaporamas de présentation

Predicting Phospholipidosis Using Machine Learning

Lowe et al., Molec. Pharmaceutics, 7 , 1708 ( 2010). 1. Predicting Phospholipidosis Using Machine Learning. Robert Lowe (Cambridge) John Mitchell (St Andrews) Robert Glen (Cambridge) Hamse Mussa (Cambridge) Florian Nigsch (Novartis).

641 views • 50 slides

Patrick An Introduction to Medicinal Chemistry 3/e Chapter 2 THE WHY & THE WHEREFORE: DRUG TARGETS

Patrick An Introduction to Medicinal Chemistry 3/e Chapter 2 THE WHY & THE WHEREFORE: DRUG TARGETS. Contents 1. Cell Structure (2 slides) 2. Cell Membrane (4 slides) 3. Drug Targets (4 slides) 4. Intermolecular Bonding Forces 4.1. Electrostatic or ionic bond

1.63k views • 38 slides

Overview

Overview. Goal Introduction SiC detector background Methods TRIM simulation Results and Interpretation Radiation Damage Future Work. Goal. Develop SiC Schottky diode detectors for measurement of actinide concentrations, from alpha activities:

511 views • 31 slides

Biological Control and Invasive Invertebrates M.L. Henneman

355 views • 24 slides

GRB2

PDGFA: miR-30a /30a-5p/30b/30b-5p/ 30c de/384-5p; miR-135; miR-1/206; miR-23ab ; miR-196ab; miR-153; miR-221 /222; miR-194; miR-141/200a; miR-375

168 views • 2 slides

Growth factor

CASP9. mTOR. CDKN1A. BAD. CHUK. CDKN1B. FOXO1. MDM2. GSK3B. PDE3A/B. NOS3. Growth factor. GFR (e.g. ERBB2).

145 views • 1 slides