AI Medical Compendium Journal:
Trends in pharmacological sciences

Showing 1 to 10 of 23 articles

Developmental toxicity: artificial intelligence-powered assessments.

Trends in pharmacological sciences
Regulatory agencies require comprehensive toxicity testing for prenatal drug exposure, including new drugs in development, to reduce concerns about developmental toxicity, that is, drug-induced toxicity and adverse effects in pregnant women and fetus...

Molecular glue meets antibody: next-generation antibody-drug conjugates.

Trends in pharmacological sciences
Antibody-drug conjugates (ADCs) have revolutionized oncology by enabling the delivery of cytotoxic agents. However, persistent limitations in payload diversity and emerging drug-resistance mechanisms have spurred investigations into innovative payloa...

Data and AI-driven synthetic binding protein discovery.

Trends in pharmacological sciences
Synthetic binding proteins (SBPs) are a class of protein binders that are artificially created and do not exist naturally. Their broad applications in tackling challenges of research, diagnostics, and therapeutics have garnered significant interest. ...

How can quantum computing be applied in clinical trial design and optimization?

Trends in pharmacological sciences
Clinical trials are necessary for assessing the safety and efficacy of treatments. However, trial timelines are severely delayed with minimal success due to a multitude of factors, including imperfect trial site selection, cohort recruitment challeng...

Recent advances in generative biology for biotherapeutic discovery.

Trends in pharmacological sciences
Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the l...

Harnessing deep learning for enhanced ligand docking.

Trends in pharmacological sciences
Ligand docking (LD), a technology for predicting protein-ligand (PL)-binding conformations and strengths, plays key roles in virtual screening (VS). However, the accuracy and speed of current LD methodologies remain suboptimal. Here, we discuss how d...

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction.

Trends in pharmacological sciences
Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are crit...

Advancing chemical carcinogenicity prediction modeling: opportunities and challenges.

Trends in pharmacological sciences
Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challen...

Computational and artificial intelligence-based methods for antibody development.

Trends in pharmacological sciences
Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has signi...

Challenges and opportunities associated with rare-variant pharmacogenomics.

Trends in pharmacological sciences
Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to ...