Collaborating for R&D Success in Drug Discovery

Collaboration in drug discovery is becoming more important as pharmaceutical R&D becomes more complex, specialised, and data-intensive.

Successful collaboration depends on far more than bringing organisations together. Partner selection, timing, governance, knowledge sharing, and communication can all shape whether collaborative R&D creates meaningful innovation.

Image

Why Drug Discovery Is Becoming a Collaborative Science

Drug discovery has never been a truly isolated activity. Pharmaceutical companies have long relied on universities, biotechnology companies, research institutes, technology providers, and other specialist organisations to access capabilities that would be difficult or inefficient to build internally.

The economics of today make this model of R&D collaboration even more important. Developing an approved medicine can take more than 10 years and up to U.S. $2.6 billion, creating strong incentives to share expertise, technology, and risk.

Yet there is a striking gap between the importance of collaboration and its actual use.

Only five per cent of 138 drugs approved by the U.S. Food and Drug Administration (FDA) and filed by the 20 largest biopharma companies between 2015 and 2021 were developed collaboratively.

That suggests the industry may still be underusing collaborative models despite their potential value.

The COVID-19 pandemic demonstrated what becomes possible when barriers between organisations fall quickly, with almost one-third of vaccine candidates during the pandemic being developed through partnerships.

The lesson for conventional drug discovery is not that every programme should become an open collaboration. It is that the right external capability, introduced at the right point, can materially change the economics and scientific trajectory of an R&D programme.

This is particularly relevant to areas such as:

• AI-enabled drug discovery and computational biology
• Novel therapeutic modalities
• Biomarker and target discovery
• Precision medicine
• Advanced drug delivery
• Rare disease research
• Human-relevant preclinical models

Analysis of AI drug discovery investment similarly shows how pharmaceutical organisations are increasingly evaluating technologies according to their ability to solve specific R&D problems rather than simply their technical novelty.

What the Evidence Says About Collaborative Drug Discovery

The 2025 pharma R&D collaboration analysis by Wu, Knockaert, and Blasi is particularly useful because it does not treat collaboration as a single activity. The researchers reviewed 737 papers identified through Web of Science and selected 74 articles for detailed analysis.

The authors then examined collaboration across three stages: Initiation, implementation, and closure. They also distinguished between homogeneous collaborations, where partners come from the same broad organisational category, and heterogeneous collaborations involving different types of organisations.

Of the reviewed literature, 54% concerned homogeneous collaborations and 46% heterogeneous collaborations.

The evidence also showed a different emphasis depending on the type of partnership. Research on homogeneous collaborations concentrated heavily on how alliances are initiated, while research on heterogeneous collaborations focused more strongly on implementation.

That single analysis alone suggests that R&D challenges change once organisations with different cultures, incentives, and capabilities begin working together.

Image

The timing of collaboration matters

One of the most important aspects for R&D collaborations concerns when partners enter a programme.

Early collaborations can help teams test technological, commercial, and scientific assumptions before significant resources are committed. They can also eliminate weak candidates earlier, allowing stronger programmes to progress towards clinical development.

Later partnerships may instead arise because a programme requires additional funding or capabilities.

That creates the strategic question of whether an external partner should be brought in after an internal programme has been de-risked, or before the most important decisions have been made?

There is no universal answer. The research suggests that the appropriate point depends on the technology, the maturity of the science, the capabilities already available internally, and the type of partner involved.

However, the collaboration timing should be treated as a portfolio decision, rather than an administrative step taken once an internal programme reaches a predefined stage.

Partner selection is more than a capability checklist

Internal readiness is also incredibly important.

Successful collaboration requires organisations to have sufficient absorptive capacity, R&D capability, relevant employee skills, prior collaboration experience, and intellectual property protection.

These capabilities help a company understand, evaluate, and apply knowledge coming from outside its organisation, and create an important distinction between having access to external science and being able to use it.

For example, a pharmaceutical company might partner with a highly capable AI, biotech, or academic organisation. But if its internal teams cannot interpret the resulting data, integrate the technology into workflows, or make decisions based on the new knowledge, the partnership may generate little practical value.

Collaboration therefore requires internal capability as well as external capability.

As pharmaceutical R&D becomes more interdisciplinary, so too will this become more important.

A drug discovery programme may now combine medicinal chemistry, structural biology, genomics, machine learning, high-throughput screening, biomarker science, and translational research. No single organisation will necessarily lead every component.

View interview How Should Pharma Companies Audit AI Vendors?

How Should Pharma Companies Audit AI Vendors?

“This is one of the riskiest industries there is because you're dealing with human lives, you're dealing with experimental protocols, and you're dealing with regulatory bodies where you might not get another shot at that clinical trial."

Image

Heterogeneous Partnerships Could Open New R&D Pathways

The distinction between homogeneous and heterogeneous collaboration is particularly relevant for modern drug discovery.

A pharmaceutical company working with another pharmaceutical company may share similar organisational structures, regulatory knowledge, and commercial objectives, while a partnership between a pharma company and a university, hospital, patient organisation, or technology company can be very different.

These heterogeneous collaborations can bring complementary knowledge that does not already exist within the pharmaceutical organisation.

For heterogeneous collaboration, implementation should be the main focus.

Governance, managerial mechanisms, innovation networks, knowledge utilisation, and communication all influence whether differences between partners become productive or disruptive.

The potential upside is tremendous. Academic researchers may contribute disease biology or novel mechanisms. Biotech companies may provide specialised platforms. Pharma can contribute development expertise, regulatory knowledge, manufacturing capability, and access to global clinical infrastructure.

The challenge is the integration of these assets without creating unnecessary organisational complexity.

The strongest partnership is therefore not necessarily the one with the most participants, but rather the one where each participant contributes something difficult to replicate elsewhere.

That principle is becoming particularly relevant as pharma companies build external innovation networks around emerging technologies.

From a regulatory standpoint, the FDA itself provides a useful example. Its scientific public-private partnerships bring together government, academia, industry, patient organisations, and other stakeholders to address scientific gaps in drug development. These collaborations have supported work across areas including biomarkers, imaging, clinical outcome assessments, genetic testing, and computational and animal models. 

This heterogeneous partnerships model illustrates a broad shift that collaboration can be used not only to develop individual medicines, but also to build the scientific infrastructure required for future medicines.

Governance Determines Whether Collaboration Delivers

Finding the right partner is only the beginning.

In pharma R&D, collaboration performance is strongly influenced by how the relationship is managed once it begins. Governance, communication, trust, knowledge sharing, and clearly defined responsibilities all become critical as projects move from initiation into implementation.

This is particularly important in heterogeneous partnerships. Different organisations can have very different objectives, decision-making structures, risk tolerances, and approaches to intellectual property.

A university may prioritise scientific publication. A biotechnology company may prioritise platform development and future investment. A pharmaceutical company may be focused on development milestones, regulatory requirements, and commercialisation.

These objectives do not have to conflict. But they need to be recognised early.

Trust, communication, knowledge sharing, and organisational mechanisms are the most important elements of effective collaborative relationships.

This means drug discovery collaboration agreements should do more than establish ownership and milestones. They should create a practical way for how scientific decisions will be made, how information will move between organisations, and how disagreements will be resolved.

Knowledge sharing is a strategic capability

Drug discovery partnerships generate value partly through knowledge transfer.

However, knowledge does not automatically move between organisations simply because a contract exists. Teams need mechanisms that allow scientific findings, experimental data, technical expertise, and lessons from failure to reach the people who can use them.

This becomes more difficult when collaborations involve multiple disciplines.

A computational team may generate a target hypothesis, while a biology team needs to validate it. Medicinal chemists then need to translate the biological insight into molecules and translational scientists may need to establish whether the mechanism is relevant to patients.

Image

The value of collaboration increases when information can travel across these boundaries without losing context.

That has implications for R&D infrastructure, too. Shared data environments, interoperable systems, agreed terminology, documented workflows, and clear data governance is as important as the scientific relationship itself.

This is one reason collaboration is increasingly intertwined with digital transformation. AI-driven drug discovery, for example, can generate large volumes of predictions and candidate hypotheses. The value of these, however, depends on whether different scientific teams can reproduce, interpret, and act on them.

Collaboration in Drug Discovery Creates New Risks

Collaboration in drug discovery is not automatically beneficial.

Several factors can complicate collaborative R&D, including differences between partners, coordination requirements, knowledge-management challenges, and the need to protect intellectual property.

There is also a strategic risk in partnering simply because a capability is fashionable.

An external platform may appear attractive because it uses generative AI, automation, novel screening technology, or another emerging approach. But the relevant question for a drug discovery programme is not whether the technology is advanced.

It’s whether the partnership improves a workflow involving a particular research decision that truly matters.

That could mean identifying better targets, reducing experimental cycles, improving candidate selection, generating stronger translational evidence, or eliminating weak candidates earlier.

The collaboration emphasis should be on absorptive capacity and it reinforces that organisations need sufficient internal expertise to recognise the value of external knowledge and integrate it into their own innovation processes.

View interview AI in Pharma: Why the Future of Healthcare Starts With Patients, Not Tech

AI in Pharma: Why the Future of Healthcare Starts With Patients, Not Tech

Kate O’Reilly, President & Chair of the Healthcare Businesswomen’s Association (HBA) Dublin-Ireland Chapter & Healthcare Transformation Partner at Roche, discusses AI in pharma, patient engagement, and the future of healthcare innovation.

Image

From Drug Discovery Alliances to R&D Ecosystems

There is a broader change in how pharmaceutical companies think about innovation.

The traditional model places most of the discovery process within the boundaries of one organisation. External relationships are then added around specific needs.

A more networked model treats the external ecosystem as a strategic extension of internal R&D.

That does not mean outsourcing discovery. It means deliberately combining capabilities.

One discovery programme might retain target strategy and portfolio decisions internally while accessing external expertise in computational biology, novel screening, structural analysis, biomarkers, or disease modelling. Another programme may follow a completely different configuration.

The important point is flexibility.

However, research into pharma collaboration and partnership remains fragmented, with different studies examining different stages, partner types, and outcomes. Much further analysis is needed to understand how collaboration evolves across the full drug innovation process.

For an industry where uncertainty is not necessarily a reason to wait, this is an imperative reason to measure collaboration rigorously.

For R&D success, the following collaboration metrics should be tracked:

• Time from partnership formation to meaningful scientific output.
• Number and quality of validated discoveries generated.
• Progression of externally generated candidates.
• Knowledge transferred into internal programmes.
• Development milestones influenced by external capabilities.
• Cost and time saved through collaboration.
• Reasons partnerships succeed, stall, or terminate.

Tracking these metrics will move collaboration away from relationship management and towards measurable R&D performance.

Collaboration in Drug Discovery Should be Part of an R&D Strategy

For collaboration to be part of a wider drug discovery strategy, there are several practical principles based on the R&D priority at that time in the specific programme:

This is especially relevant as drug discovery becomes more distributed. The next generation of medicines may depend on combinations of capabilities that no single organisation possesses and how effectively they can be orchestrated.

Collaboration Could Become the Most Important Part of the Drug Discovery Engine

Collaboration is more than simply an efficient way to fill capability gaps. Done well, it can change how pharmaceutical R&D works.

The opportunity is particularly strong where emerging science moves faster than internal organisations can build expertise.

Partnerships can provide access to new biological insights, technologies, datasets, and specialist knowledge. But those benefits only materialise when companies have the internal capability and governance required to absorb them.

The result is a more strategic view of collaboration with the question being whom you should pharma partner with? What kind of scientific problems require a network rather than a single organisation?

At Pharmatica we understand the significance of collaboration as the Drug Discovery Loop becomes less linear and more connected. We know that the pharma companies best positioned to capture the next wave of innovation will be those that can combine internal scientific depth with carefully selected external capabilities while maintaining the governance needed to turn shared knowledge into better drug discovery decisions.

Pharmatica: Insight. Connection. Impact.

Image