Insights

Pharma 2024: Advancements, Acquisitions, and Collaborations are Shaping the Future of Drug Discovery

By Arnon Fluksman, PhD, Scientific Analyst, AION Labs

The intersection of AI and computational technologies with pharmaceutical innovation continues to spur advancements in drug discovery and development. This year has seen a wave of investments, strategic acquisitions, and technological breakthroughs that are reshaping the global biotech landscape.

Here is a rundown of some of this year’s most significant developments and partnerships that are driving innovation in AI-driven pharmaceutical research.

Investments and Acquisitions

Deep tech startup VantAI teamed up with Bristol Myers Squibb to accelerate the discovery of molecular glue drugs using generative AI. The $674 million partnership sees the pharma giant leveraging VantAI’s geometric deep learning capabilities for targeted protein degradation, which can lead to the discovery of new small molecule therapeutics.

Meanwhile, AstraZeneca completed its acquisition of Fusion Pharmaceuticals, expanding the big pharma’s oncology portfolio. The $2.4 billion purchase gives AstraZeneca a foothold in the radiopharmaceutical drug space, strengthening the company’s position in a rapidly evolving market currently led by Novartis.

In a similar vein, Johnson & Johnson concluded its $2 billion purchase of Ambrx Biopharma. Leveraging Ambrx’s synthetic biology platform, J&J is enhancing its cancer treatment pipeline with next-generation  antibody drug conjugates (ADCs).

J&J is not the first to buy in to ADCs this year. In January, Korean food and healthcare corporation Pan Orion got on the ADC wagon, investing $415 million for a 25% stake in LegoChem Biosciences. LegoChem’s ConjuALL platform overcomes the existing limitations of ADC therapies.

Xaira, a new AI-driven drug discovery company co-founded by Dr. David Baker and researchers behind RFdiffusion and RFantibody, launched with $1B in funding. Xaira will leverage machine learning, data generation, and therapeutic development to create a cutting-edge platform for drug discovery based on groundbreaking models for protein and antibody design.

Finally, Vertex Pharmaceuticals has strengthened its position in immunology. In April, the Boston-based pharmaceutical company acquired Alpine Immune Sciences for $4.9 billion, the industry’s largest acquisition this year. The deal will enable Vertex to further develop Alpine’s lead asset, povetacicept, currently in Phase II trials for IgA nephropathy.

Computing Breakthroughs

In May, clinical-stage biotech company Recursion Pharmaceuticals unveiled BioHive-2, the fastest supercomputer wholly owned and operated by a pharma company. Developed in collaboration with NVIDIA, BioHive-2 enhances AI-powered drug discovery by allowing Recursion to train larger AI models more efficiently while supporting multiple parallel projects simultaneously.

A collaboration between quantum computing startup Zapata Computing and biotech company Insilico Medicine showcased the first use of a 16-qubit IBM quantum device for quantum-enhanced generative AI. The multi-partner collaboration, including Foxconn and the University of Toronto, has produced millions of potential cancer drug candidates specifically targeting KRAS inhibitors, with two molecules showing unique structures and improved efficiency compared to traditional methods.

AI for Target Identification

AstraZeneca and BenevolentAI extended their collaboration in AI-powered drug discovery, showcasing BenevolentAI’s AI-augmented drug discovery capabilities with a novel target for systemic lupus erythematosus (SLE). Owkin also expanded its collaboration with Sanofi to leverage AI for precision drug discovery in immunology, with the aim to tailor treatments by identifying gene targets and disease indications.

Earlier this year, Enzolytics partnered with Khalpey AI Lab to advance organ regeneration and longevity research, leveraging AI to identify biomarkers associated with aging and organ failure. Meanwhile, Ono Pharmaceutical’s and InveniAI entered a research collaboration agreement to leverage AI and machine learning to identify new therapeutic targets for a range of diseases.

The Future of Clinical Trials

In collaboration with NVIDIA, Sartorius AG is developing AI models for stem cell-derived organoid prediction. Leveraging NVIDIA’s Clara suite and DGX platform, the partnership aims to replace animal models in drug discovery and precision medicine.

In a recent study, Connecticut-based Phesi has demonstrated how AI-powered digital twins could replace standard control arms in clinical trials. The clinical development analytics company constructed a twin cohort of 2,042 chronic graft-versus-host disease (cGvHD) patients drawn from its Trial Accelerator platform, showing the potential to streamline processes and reduce patient burden in cGvHD and similar conditions.

RNA Therapeutics: The Next Frontier

In June, Envisagenics secured $25 million in Series B funding to advance its AI-enabled RNA splicing therapeutics platform. Combining machine learning algorithms with high-performance computing, the SpliceCore platform can identify novel and disease-specific alternative splicing isoforms.

At the same time, GSK dropped $50 million to buy out Elsie Biotechnologies. The acquisition follows the companies’ previous collaboration agreement and will accelerate GSK’s oligonucleotide pipeline, including its work in hepatitis B and steatotic liver disease.

Making June the month for RNA development deals, Roche invested $42 million with Ascidian Therapeutics to explore RNA exon editing drugs. The agreement could eventually see Ascidian receive up to $1.8 billion in milestone payments, as Roche works to develop novel therapeutics for neurological conditions.

New AI-Driven Platforms and Models

So far this year, we’ve seen several new AI platforms launched, spelling even more promise for pharmaceutical research and development:

Google Research and DeepMind announced the creation of Tx-LLM, a new drug discovery and therapeutic development tool. Fine-tuned from the Med-PaLM 2 large language model, Tx-LLM has outperformed state-of-the-art models in multiple tasks.

Iambic Therapeutics’ NeuralPLexer platform has demonstrated its ability to predict protein-ligand complex structures, outperforming systems like AlphAfold2. Iambic’s suite includes the ProPANE platform, which uses a graph neural network deployed across multiple drug properties.

Basecamp Research partnered with NVIDIA to develop the BaseFold deep learning model for protein structure prediction. BaseFold is anticipated to accelerate AI-driven drug discovery by predicting the 3D structures of large, complex proteins with unprecedented accuracy.

Also this year, DeepMind and Isomorphic Labs launched AlphaFold3 to revamp drug discovery. The new AI model can predict molecular structures with higher accuracy, managing how proteins interact with ligands, ions, DNA and RNA.

Finally, EvolutionaryScale unveiled ESM3, a generative AI model to advance protein research and discovery. Comprising 98 billion parameters, it can predict protein sequence, structure, and function using techniques inspired by natural language processing models.

The pharmaceutical industry is on the brink of a major transformation, with AI playing a pivotal role in revolutionizing drug discovery and development. GenAI is accelerating key processes like target identification, lead optimization, and preclinical testing, drastically reducing the time and costs traditionally involved. Meanwhile, foundational models such as BioGPT and Med-PaLM are making strides in analysing complex data across various domains, from microscopy to protein folding, while multimodal models open the door to advanced in silico experiments. In Q2 2024 alone, AI-related deals grew by 14% compared to the previous year, and AI-related patent applications saw an 11% annual rise.

With research and early discovery projected to contribute $15-28 billion annually, major players like Sanofi, AstraZeneca, and Johnson & Johnson are ramping up their AI hiring efforts. This growth in AI technologies reflects the industry’s continued commitment to faster drug development and improved patient outcomes, positioning AI to redefine the future of healthcare.

2024 has already seen an unprecedented acceleration of AI-driven pharma innovation. From drug discovery and target identification to clinical trials and RNA therapeutics, AI and computational biology are redefining the industry. Through the remainder of this year, we can expect to see the emergence of faster and more effective treatments tailored to individual patient needs.