Novel Antibodies
Dual-Targeting Bispecific Antibodies(BsAbs), Nanobodies and Antibody drug conjugates in Modern Therapeutics
Role of Novel Antibodies in Therapeutics
Novel antibodies, such as bispecific antibodies, nanobodies, and antibody-drug conjugates (ADCs), are characterized by multispecific targeting, increased potency and stability, and non-natural effector functions, contributing to broader therapeutic benefits. Nanobodies serve as precise targeted vehicles, ADCs act as potent therapeutic agents, and bispecific antibodies stand out as versatile therapeutic tools. Explore the next- generation of antibody therapeutics for advanced medical applications.
Diverse Antibody based therapeutics reshaping the drug landscape
Representing innovative class of drugs including Bispecific monoclonal antibodies, Antibody drug conjugates and Nanobodies
Aganitha’s Point of View
Next-Generation Antibodies: Tailoring Novel Antibodies for Precise and Potent Therapies using AI, ML and in silico tools
Camelid-specific Sequence Analysis
Utilizing camelid-specific antibody sequence databases specially VHH to provide valuable insights into the sequence diversity and common motifs in natural camelid antibodies, guiding the design/virtual screening of synthetic nanobodies.
VHH Framework Optimization
Utilizing ML models to optimize the framework regions(VHH domains) for enhanced stability and solubility, which are critical due to the absence of the light chain in nanobodies.
CDR Loop Refinement
Utilizing specialized computational approaches such as diffusion based models to specifically model and refine the CDR loops of nanobodies, which are crucial for antigen binding.
Aggregation Control
Streamlining the identification and modification of aggregation-prone regions to ensure stability, solubility, and efficacy of antibodies. Thus, facilitating early screening and targeted mutagenesis for enhancing the expression and solubility of antibodies.
Implementing In Silico Affinity Maturation
Employing in silico affinity maturation, and utilizing Generative AI , ML and computational methods to enhance the binding affinity of antibody candidates to their targets, a critical step in effective therapeutic development.
Ensemble Approach for Binding Affinity Prediction
Employing an in silico ensemble approach, utilizing a variety of computational techniques to predict the efficacy of molecule-target interactions, essential for successful therapeutic development.
Advancing Molecular Modeling Techniques
Utilizing physics-based and diffusion algorithms for advanced molecular modeling, the platform efficiently deciphers complex interactions of bispecific antibodies, nanobodies for optimizing their therapeutic efficacy and ensures targeted precision of ADCs.
Conducting Ongoing Developability Assessments
Using Generative AI with Developability constraints for generating candidates. Continually conducting comprehensive developability assessments to ensure antibody candidates exhibit effective binding capabilities and are suitable for safe and efficient drug development.
Enhancing Antibody Design with AI
Integrating Generative AI and machine learning to enhance and streamline the antibody design process, surpassing traditional methodologies for enabling predictive and precise antibody engineering.
Ensemble Approach for Binding Affinity Prediction
Incorporating computational virtual screening, a rapid technique for evaluating a library of antibody sequences and structures to identify potential candidates for further development.