Life Science

We are witnessing a shift toward a computational “predict-first” approach to drug discovery and materials science research, driven, in part, by Schrödinger's game-changing force field developments over the last 20 years.

With input from the academic community, Schrödinger has developed an affordable offering designed to empower academic researchers to apply FEP+ to basic research activities.

LiveDesign's cloud-based informatics and discovery environment has expanded to support scientists and teams in the discovery of novel biologics.

The human body is designed to protect against outside invaders. Skin acts as a physical barrier to keep viruses and bacteria from entering the bloodstream. Our immune system creates antibodies to fight specific germs. The blood-brain barrier provides another important defense mechanism—a membrane of cells that filter blood and guard the brain from toxins. Transporter

Developing medicines takes teamwork, and modern drug discovery teams are often spread out across the globe. At Takeda, chemists collaborate with colleagues at other sites in different time zones, as well as with external partners and CROs. To improve molecular design processes and to keep teams operating seamlessly, Takeda adopted Schrödinger’s LiveDesign, a cloud-native, collaborative

私たちは、チロシンキナーゼ2（TYK2）阻害剤プログラムを主導したSchrödingerとNimbus Therapeuticsの初期チームメンバーに話を伺いました。このプログラムで開発された経口選択的アロステリック阻害剤のNDI-034858は、乾癬で最近成功を収めた第2b相試験に続き、複数の自己免疫疾患の治療薬として評価されています。2022年12月24日、武田薬品は、Nimbus Therapeuticsの100%子会社であるNimbus Lakshmi, Inc.とTYK2阻害剤、NDI-034858を60億ドルで買収することを発表しました。本記事では、彼らが直面した課題と、それをどのように克服してベストインクラスの候補となる医薬品を開発するまでに至ったのかをご紹介します。そこには、これまでとはまったく違うやり方で創薬を進める彼らの的確なビジョンとマインドセットがあったのです。

We sat down with some of the original team members from Schrödinger and Nimbus Therapeutics who led the tyrosine kinase 2 (TYK2) inhibitor program that resulted in NDI-034858, an oral, selective allosteric inhibitor being evaluated for the treatment of multiple autoimmune diseases following successful recent Phase 2b results in psoriasis. On December 24, 2022, Takeda

Artificial intelligence (AI)/machine learning (ML) methods can now predict the 3D folded structures of many proteins with surprising accuracy, potentially shortening the time from inspired ideas to actual medical treatments. What used to be a long and uncertain path to obtain the structures of targets for drug discovery can now be reduced significantly. These advances

For people with chronic medical conditions, a reliable therapeutic that treats their disease while preserving a good quality of life can be transformative. Patients are thankful when a medication helps relieve symptoms, even if that treatment comes with dosing challenges such as injections or infusions required for most biologic drugs. In our current era of

In science, working collaboratively is absolutely essential to increase the odds for success. Collectively pooling ideas can lead to better solutions, faster. If we want to achieve more, we need to join efforts and work smarter, not just harder. This is especially true in modern digital chemistry, where innovators might be searching for one single

Drug hunters face enormous challenges in their efforts to bring new treatments to patients. The traditional process of designing molecules that have the right properties to become safe and effective drug candidates takes approximately five years, requires thousands of molecules to be synthesized in the laboratory for preclinical testing, and costs many millions of dollars.

Antibodies are specialized protein molecules naturally made by our immune system to help fight off infections. Like a well-aimed slingshot, an antibody is capable of hitting and neutralizing the intended target with astonishing precision. Vaccine-induced antibodies and laboratory-produced monoclonal antibodies (mAbs) have transformed healthcare for millions of individuals suffering from serious and life-threatening diseases, including

Global health has never been more top of mind than it is now in the age of Covid-19. We are living through a rare moment when the investments and precautions taken by nations to protect their citizens’ health are daily present among our news headlines. But paradoxically, many of the most pressing global health challenges

On January 14, 2021, Schrödinger acquired XTAL BioStructures, Inc., a private company based in the Greater Boston area that provides structural biology services, including biophysical methods, protein production and purification, and X-ray crystallography, to the pharmaceutical and biotechnology industries to enable structure-based drug discovery. XTAL was founded in 2005 by Robert Suto, Ph.D.  We sat

Arguably one of the most important challenges facing the pharma industry today is the efficient adoption of digital platforms to improve the discovery and development of new medicines. At Bayer, we have tackled this challenge with widespread digitalization efforts across our R&D organization, based on the firm belief that we must evolve in order to

We recently spoke with several members of our drug discovery (DDAG) team to understand their perspectives on why they chose to pursue their drug discovery careers at Schrödinger.

Like many things in life, early-stage drug discovery is a numbers game. The more chemical space that can be explored earlier in the discovery process, the greater the likelihood of finding novel, biologically active molecules that meet key parameters for clinical advancement.

When NVIDIA invented the graphics processing unit (GPU) back in the 1990s, it wasn’t just a breakthrough — it was a revolution. GPUs were originally intended to improve computer graphics. They certainly did that, but more realistic video games were just the start.

In 2017, Takeda and Schrödinger joined forces to accelerate the discovery of novel, differentiated therapeutic compounds using Schrödinger’s physics-based computational platform, and having access to Takeda’s deep, innovative expertise and experience in drug discovery.

In 2017, Takeda and Schrödinger joined forces to accelerate the discovery of novel, differentiated therapeutic compounds using Schrödinger’s physics-based computational platform, and having access to Takeda’s deep, innovative expertise and experience in drug discovery.

In late 2019, Schrödinger and AstraZeneca teamed up with the aim of accelerating the discovery of novel therapeutic compounds by improving the throughput of binding affinity predictions.

Imagine that the entire universe of “chemical space”—all the molecules that could ever possibly be created—is the size of an ocean. How much have we explored so far? Much less than a single drop of water.

As a Med Chemist at one of the largest pharmaceutical companies in the world, I was well aware of the advantages of using computational chemistry software to render models and visualize ligand binding to proteins...

Animated movies have served as a dominant form of my family’s entertainment for more than a decade, so we were thrilled with the recent release of “Toy Story 4”. Our list of favorites is long — but the original Toy Story holds a seminal place both in our family’s memory and in film history.

I was both inspired and humbled yesterday during The Boston Cancer Summit hosted by the Fred Hutch Cancer Center and Luke Timmerman. An impressive gathering of medical researchers, biopharma executives, policy experts, investors, and innovators congregated in the aptly named “Guiding Star” conference room at Alexion’s HQ in Boston’s Seaport District.

A picture is worth a thousand words. How does this long-standing idiom – popularized by early 19th century journalists and advertisers, and attributed to Confucius – apply to drug discovery and compound design? In structural biology, as in many fields, complexity can be simpler to understand when represented graphically.