From minor aches to major ailments, there is a vast variety of medicines for helping us heal. But did you ever wonder how much research goes into every single drug we take? Developing new drugs is a long and expensive process. It takes an estimated $2.8 billion and up to 15 years to take a new drug through to regulatory approval ^{[1] [2]}.

The overwhelming majority of today’s pharmaceuticals are ‘small molecule’ compounds, made of a few hundred atoms or less. Between 2010 and 2020, 76% of new drugs approved by the US Food and Drug Administration (FDA) were small molecules ^[3]. Compared to larger biological therapies, the extra-small size of these drugs makes it easier for them to enter cells and reach targets in the body.

The diverse compounds in this group share little in common except for their size and the fact that they’re made from synthetic chemical reactions. A popular example is aspirin – one of the oldest and most widely used medicines in the world. Many of the new cutting-edge drugs for treating illnesses, such as cancer, autoimmune diseases and depression, are also small molecules. Currently, countless more are being developed to prevent and treat different conditions.

At the heart of small molecule drug discovery is chemical synthesis, a complex, laborious  process for creating new molecules – and a key bottleneck for advancing new medicines to the clinic. But recent developments in AI-based software offer unprecedented opportunities for accelerating and facilitating this stage of drug discovery.

Developing drug molecules is a slow, iterative process that involves sifting through thousands of candidates to find the most suitable one. Researchers must perform numerous experiments across several rounds of optimization.

The process begins with biologists identifying a promising new drug target – such as an enzyme involved in a disease-critical pathway. Next, they search for potential drug candidates that can specifically interact with the desired target to cause a beneficial change. But to become a successful drug, a molecule must also fulfill several other criteria, including non-toxicity, solubility, and stability.

Researchers typically start by screening libraries containing many thousands of compounds, with the help of high-throughput assays and computer software. The aim is to identify several initial ‘hit’ compounds with activity against the target.

Next comes the process of lead optimization, in which medicinal chemists modify the core chemical structure of a ‘hit’ compound by removing parts of a molecule or adding others to optimize its properties. This stage requires a thorough understanding of the chemical reactivity of complex molecules.

 Each ‘hit’ then undergoes a series of tests to explore its suitability as a drug. In the end, a single compound with the best qualities – the drug candidate – is selected. It is then put through further testing to ensure the feasibility of its large-scale synthesis and manufacturing. If it passes, the drug is ready for pre-clinical testing – and will hopefully progress to clinical trials on people.

Unfortunately, most compounds never reach the clinic, with vast numbers discarded at some stage along the way.

Synthetic chemistry has primarily relied on the expertise and creativity of medicinal chemists to devise innovative approaches for synthesizing new molecules. However, recent advances in AI have the potential to enhance their success rates and accelerate drug discovery projects. 

According to Dr.  Ewa Gajewska, "We are developing breakthrough technologies that can save chemists significant time and enable companies to optimize costs while expediting the delivery of therapeutics to patients."

SYNTHIA^® retrosynthesis software, designed by a team of chemists and computer scientists over a span of 21 years, is powered by advanced algorithms. These algorithms enable chemists to access and leverage extensive repositories of chemical synthesis data collected over decades of research.

"Our novel drug discovery software empowers chemists with diverse avenues to explore for molecule synthesis," explains Gajewska . “The software examines both established and innovative solutions, filters out ineffective options, and focuses on the most promising pathways.”

By harnessing more than 110,000 meticulously crafted reaction rules, the tool systematically evaluates retrosynthetic possibilities while considering past achievements, future opportunities, and available starting materials. 

"We have a team of Ph.D.-level synthetic chemistry experts that develop tailored rules, differentiating our tool from others in the field."

SYNTHIA^® retrosynthesis software provides invaluable insights that guide chemists towards the most favorable execution path. This approach minimizes costs, reduces the number of steps involved, and maximizes the likelihood of successfully obtaining the desired molecule with optimal properties. As a result, it significantly shortens the time required for chemists to identify a viable laboratory approach.

To contact us, learn more, or request a demo, please click HERE

Read more about SYNTHIA^® retrosynthesis software in our article Lab Manager.

What if you could use AI and machine learning to design plausible routes to complex natural products and find the best synthesis method? Learn how SYNTHIA^® retrosynthesis software helped scientists reach their product goals faster in Nature Research (Publishing).^®

References:

[1] https://syrris.com/applications/drug-discovery-and-development/
[2] https://www.biopharmatrend.com/post/67-will-small-molecules-sustain-pharmaceutical-race-with-biologics/

Further information about SYNTHIA™ retrosynthesis software can be found here: https://www.sigmaaldrich.com/UK/en/services/software-and-digital-platforms/synthia-retrosynthesis-software