By Haydn Boehm

With biologics dominating headlines, one might be tempted to think that small molecule discovery is fading, or that there is little viable target and drug space left uncovered. This simply isn’t the case. In fact, small molecule drug discovery is entering a renaissance.

In 2021, 62% of new drug approvals were for small molecules, including new treatments for HIV, cancer, infections, heart and kidney disease, neurological disorders, and more. While market share may shift as biologics discovery grows, small molecules—both alone and as part of mixed entities—will undoubtedly remain reliable and cost-effective treatment options for a wide range of common conditions.

There is, in fact, target and drug space beyond what has traditionally been possible; but stagnated approaches to small molecule drug discovery just won’t get us there. Innovation is key. Just as novel methodologies and technologies have propelled biologics from theoretical concepts into the realm of available treatments, so will they help deliver a second coming of small molecules. There is much potential to be realized in small molecule drug discovery as we see advances in screening technologies, target exploration, and computational methods, including AI.

But barriers to innovation abound: rising costs, technology gaps, data management challenges, fragmented workflows, productivity drains, increasing CRO reliance.  How can organizations overcome these challenges to reap the benefits of a small molecule drug discovery renaissance?

Keys to Success in Small Molecule Drug Discovery

Success in the small molecule drug discovery renaissance will hinge upon a few variables:

Data management
We must keep pace with advances in computational design by also investing in next-generation lab-data management technology that lets us efficiently merge, manage, and use all data, no matter where there were derived.

Research Collaboration
We must support novel cross-discipline research paradigms that help scientists move beyond the traditional scope of small molecule discovery to better understand targets.

Workflow Optimization
We must optimize small molecule drug discovery workflows—from hit identification to lead optimization to candidate selection—in order to meet increasing demands to improve cycle times, reduce costs, and find better candidates, faster.

Let's look at each of these variables at length.

1. Data Management in Small Molecule Drug Discovery
   
   The ubiquity of cloud computing means it’s no longer just the biggest players with access to the computational resources needed to undertake massive-scale virtual exploration. Even the smallest start-ups can virtually screen millions of compounds to uncover hits or perform lead optimization analyses once deemed out-of-reach due to their intense computational demands.

But when do we transition from the in-silico world back to the wet lab? Sooner, rather than later, many experts suggest. By testing and developing virtual leads in the web lab, researchers can learn more about them and help inform subsequent phases of discovery and development.

In order to do such, teams need to be outfitted with technology infrastructure that can efficiently handle the volume and variety of data flowing out of computational exploration and merge it with the scores of data coming from lab exploration, whether that be structural data, assay results, toxicology assessments, etcetera.

By some estimates, researchers can spend up to 42% of their time on administrative tasks, rather than on actual research. Freeing just a fraction of this time can pay off in dividends. When teams have easy access to a united source of trustworthy data, they gain better insight into the big picture and researchers can optimize time at the bench, instead of wasting time manually searching and exchanging data, repeating experiments, or following dead leads.

2. Research Collaboration in Small Molecule Drug Discovery

Collaboration will also play a key role in extending the scope of traditional small molecule research paradigms.

Beyond investing in the latest and greatest technologies to enable scientific exploration, organizations must also facilitate teamwork amongst research groups who have historically worked in relative isolation, with different workflows, tools, and data types.

For example, to improve target understanding, proteomics teams exploring new or poorly understood protein pockets on well-validated targets must be given tools to collaborate and share data with chemistry teams searching for novel compounds that selectively interact with those pockets. If any of those team members are from outside CROs, security also becomes a key concern.

The best way to facilitate this type of collaborative, boundary-breaking research is with an end-to-end research informatics platform that enables organizations to:

• Unite proteomics and chemistry teams (along with all their tools and data) on a single platform that not only improves collaboration, but also reduces technical debt and total cost of ownership
• Have one place to manage everything—design, synthesis, sample logistics, QC analysis, screening, and SAR analysis
• Get up-and-running quickly and seamlessly scale as projects, data, and teams grow
• Collaborate with CRO partners using secure cloud data exchange

3. Workflow Optimization in Small Molecule Drug Discovery

Industry estimates indicate that there has been a 12% increase in R&D costs in recent years. The burden of offsetting these increases tends to trickle down, leaving small molecule discovery teams with no other choice but to optimize their processes.

Undoubtedly, optimization in early discovery can reduce the cost of downstream failures in clinical testing, trials, regulatory review, and commercialization. But with small molecule drug discovery workflows becoming increasingly complex and data volumes skyrocketing, it is not an easy task to get everything and everyone working together toward a common goal of finding better candidates, faster.

Haydn Boehm is Director of Product Marketing at Dotmatics, a leader in R&D scientific software connecting science, data, and decision-making. Its enterprise R&D platform and scientists’ favorite applications drive efficiency and accelerate innovation.