Combinatorial Library Design and Evaluation (Principles, Software, Tools, and Applications in Drug Discovery)
List Price:
$89.99
- Availability: Confirm prior to ordering
- Branding: minimum 50 pieces (add’l costs below)
- Check Freight Rates (branded products only)
Branding Options (v), Availability & Lead Times
- 1-Color Imprint: $2.00 ea.
- Promo-Page Insert: $2.50 ea. (full-color printed, single-sided page)
- Belly-Band Wrap: $2.50 ea. (full-color printed)
- Set-Up Charge: $45 per decoration
- Availability: Product availability changes daily, so please confirm your quantity is available prior to placing an order.
- Branded Products: allow 10 business days from proof approval for production. Branding options may be limited or unavailable based on product design or cover artwork.
- Unbranded Products: allow 3-5 business days for shipping. All Unbranded items receive FREE ground shipping in the US. Inquire for international shipping.
- RETURNS/CANCELLATIONS: All orders, branded or unbranded, are NON-CANCELLABLE and NON-RETURNABLE once a purchase order has been received.
Product Details
Author:
Arup Ghose, Vellerkad Viswanadhan
Format:
Paperback
Pages:
648
Publisher:
CRC Press (October 29, 2019)
Language:
English
Audience:
Professional and scholarly
ISBN-13:
9780367397227
Weight:
35.875oz
Dimensions:
6.125" x 9.1875"
File:
TAYLORFRANCIS-TayFran_260403050946149-20260403.xml
Folder:
TAYLORFRANCIS
List Price:
$89.99
Country of Origin:
United States
Case Pack:
10
As low as:
$85.49
Publisher Identifier:
P-CRC
Discount Code:
H
Pub Discount:
30
Imprint:
CRC Press
Overview
This text traces developments in rational drug discovery and combinatorial library design with contributions from 50 leading scientists in academia and industry who offer coverage of basic principles, design strategies, methodologies, software tools and algorithms, and applications. It outlines the fundamentals of pharmacophore modelling and 3D Quantitative Structure-Activity Relationships (QSAR), classical QSAR, and target protein structure-based design methods.
