Redesign Science Company Profile

Background

Overview

Founded in 2017, Redesign Science is a biotechnology company specializing in computational chemistry to advance small molecule drug discovery. The company integrates physics-based molecular simulations with deep learning to create generative AI models for drug discovery, focusing on developing first-in-class Protein Interaction Modulators targeting oncogenic and inflammatory diseases.

Mission and Vision

Redesign Science aims to revolutionize early-stage drug discovery by efficiently simulating protein targets at the atomic level, uncovering subtle drug opportunities beyond the reach of traditional computational models. Their mission is to leverage cutting-edge computational approaches to better understand protein dynamics for precision structure-based drug discovery and to expand the druggable proteome.

Industry Significance

By combining advanced physics simulations with AI-driven methodologies, Redesign Science addresses emerging challenges in drug discovery, particularly for novel or poorly understood targets. Their approach has the potential to significantly reduce the time and resources required in early-stage drug development.

Key Strategic Focus

Core Objectives

• Develop and optimize new therapeutics for novel or poorly understood targets.

• Reduce partners' time investment in early-stage drug discovery.

• Expand the druggable proteome through advanced computational methods.

Areas of Specialization

• First-in-class Protein Interaction Modulators.

• Targets associated with oncogenic and inflammatory diseases.

Key Technologies Utilized

• Physics-based molecular simulations.

• Deep learning and generative AI models.

• Cloud-based computational platforms.

Primary Markets Targeted

• Biotechnology and pharmaceutical companies seeking innovative drug discovery solutions.

• Therapeutic areas including oncology and autoimmune diseases.

Financials and Funding

Funding History

As of December 2021, Redesign Science has raised a total of $15 million through Seed-2 and Seed-3 financing rounds. The $5 million Seed-2 round was led by Collaborative Fund, with participation from 5Y Capital, Notation Capital, Third Kind Venture Capital, Preface Ventures, Refactor Capital, Acequia Capital, and Hawktail. The $10 million Seed-3 round was led by Kaitai Capital, joined by ZhenFund, Third Kind Venture Capital, Preface Ventures, and Collaborative Fund.

Utilization of Capital

The funds are intended to expand cloud computing infrastructure, advance core screening algorithms, scale across diverse biological target classes, and launch an internal pipeline of early-stage asset development.

Pipeline Development

Redesign Science is advancing a differentiated pipeline with first and best-in-class potential across oncology and autoimmune indications. Their approach combines first-principles physics with generative AI to unlock challenging targets.

Technological Platform and Innovation

Proprietary Technologies

• NUVO™ Matisse: Utilizes proprietary physics and AI to generate extensive and refined structural data of drug targets, enabling rapid prediction of mechanisms of action, drug binding modes, and absolute binding free energies.

• NUVO™ Picasso: Employs generative models to produce proprietary focused libraries of novel compounds, targeting discovered pockets and optimizing for physicochemical constraints.

• NUVO™ Cézanne: Leverages machine learning to scale the predictive power of physics-based models, scoring and ranking billions of ligands with high accuracy.

Significant Scientific Methods

• Enhanced sampling techniques for solving protein motion.

• Integration of molecular dynamics with statistical physics and machine learning.

• Active learning and deep learning for predictive modeling.

Leadership Team

• David Rooklin: Co-Founder and CEO.

• Haotian Li: Co-Founder and CTO.

• Andrew Avorn: General Counsel.

• Brian Petkov: Vice President of Methodology.

• Sharon Blaettler: Head of Biology.

• Scott Liu: Director of Business Development.

• Janan Zhu: Head of Engineering.

Competitor Profile

Market Insights and Dynamics

The computational drug discovery market is experiencing significant growth, driven by advancements in AI, machine learning, and molecular simulations. Companies are increasingly leveraging these technologies to expedite drug development processes and address complex biological targets.

Competitor Analysis

• Cyclica: Utilizes AI and computational biophysics to design and screen novel drug candidates, focusing on polypharmacology.

• Atomwise: Employs AI for structure-based drug discovery, aiming to predict binding affinities and identify potential drug candidates.

• DeepCure: Combines AI with high-throughput experimentation to accelerate small molecule drug discovery.

• SilcsBio: Provides computational tools for structure-based drug design, focusing on ligand binding and protein flexibility.

These competitors share a focus on integrating computational methods with drug discovery, each with unique approaches and technological platforms.

Strategic Collaborations and Partnerships

In January 2020, Redesign Science entered into a strategic partnership with OpenEye Scientific to integrate OpenEye's Orion computational platform into Redesign's drug discovery platform. This collaboration aims to enhance scalability and efficiency in drug discovery efforts.

Operational Insights

Redesign Science's integration of advanced physics simulations with AI-driven methodologies positions the company uniquely in the computational drug discovery landscape. Their proprietary NUVO™ platform offers distinct advantages in identifying novel drug targets.