Chai-1 vs AlphaFold 3 vs Boltz-2: Protein-Ligand Complex Prediction Compared

Three Models, One Problem: Predicting How Molecules Bind

Protein-ligand complex prediction is one of the most important problems in computational drug discovery. Knowing exactly how a small molecule binds to its protein target enables rational drug design, lead optimization, and virtual screening at scale. In 2024 and 2025, three models emerged as the leading tools for this task: Chai-1, AlphaFold 3, and Boltz-2.

All three use diffusion-based generative architectures to predict the 3D geometry of molecular complexes, but they differ significantly in design philosophy, accessibility, and practical usability. This comparison covers everything you need to know to choose the right tool for your research or production pipeline.

Chai-1: Open-Source Precision from Chai Discovery

Chai-1 was released by Chai Discovery in September 2024. It is a multi-modal foundation model designed specifically for molecular structure prediction, with a focus on protein-ligand and protein-protein complexes. The model achieved state-of-the-art performance on several benchmarks at launch, particularly for drug-like small molecule binding pose prediction.

• Developer: Chai Discovery
• Released: September 2024
• Architecture: modified diffusion model with trunk transformer and pair representation
• Weights: publicly available (open source)
• Access: local GPU deployment, Chai Discovery web server, or SciRouter API
• License: open source, commercial use permitted
• GPU requirement: A100 80GB minimum
• Inputs: proteins, small molecules (SMILES), protein-protein, protein-nucleic acid

Chai-1 uses a modified diffusion process that operates directly on atomic coordinates with a learned noise schedule. Its trunk transformer architecture processes both sequence and pair-level features simultaneously, allowing the model to capture long-range interactions between the protein and ligand during the denoising process. The model was trained on a curated dataset of experimental structures from the PDB, with careful filtering to avoid data leakage on benchmark sets.

AlphaFold 3: DeepMind's Universal Structure Predictor

AlphaFold 3 was published by Google DeepMind and Isomorphic Labs in May 2024 in Nature. It extends AlphaFold 2 from single-chain protein folding to general biomolecular complex prediction. The model handles the broadest range of molecular types of any structure prediction tool, including proteins, DNA, RNA, small molecules, ions, and modified residues.

• Developer: Google DeepMind / Isomorphic Labs
• Released: May 2024
• Architecture: Evoformer (Pairformer variant) + diffusion module
• Weights: not publicly available (closed source)
• Access: AlphaFold Server (alphafoldserver.com) with daily prediction limits
• License: non-commercial research only; no local deployment
• GPU requirement: Google infrastructure only (not user-deployable)
• Inputs: proteins, DNA, RNA, small molecules, ions, modified residues

AlphaFold 3 pairs its Evoformer-derived encoder with a diffusion-based structure module that generates all-atom coordinates. The Evoformer processes MSA and pair representations to build rich structural features, which then condition the diffusion process. This two-stage approach leverages evolutionary information before generating structures, which gives it strong performance across diverse molecular types.

Boltz-2: MIT's Confidence-Guided Approach

Boltz-2 was developed by researchers at MIT in collaboration with Genesis Therapeutics and released as a fully open-source model. It builds on the Boltz-1 architecture with a key innovation: confidence-guided diffusion that uses predicted confidence scores to steer the denoising process toward higher-quality structures.

• Developer: MIT / Genesis Therapeutics
• Released: 2024-2025
• Architecture: confidence-guided diffusion with pairwise attention
• Weights: publicly available (open source)
• Access: local GPU deployment or SciRouter API
• License: open source, commercial use permitted
• GPU requirement: A100 (40GB or 80GB)
• Inputs: proteins, DNA, RNA, small molecules

The confidence-guided mechanism in Boltz-2 is its distinguishing feature. During the diffusion process, the model predicts per-residue and per-atom confidence scores that are fed back into subsequent denoising steps. This self-correcting loop helps the model avoid low-confidence regions of structure space and produces more physically plausible poses, especially for flexible binding sites.

Architecture Comparison

While all three models use diffusion-based generation, their architectural choices reflect different design philosophies:

Feature Encoding

AlphaFold 3 relies heavily on MSA (multiple sequence alignment) processing through its Evoformer module, which extracts co-evolutionary signals from related protein sequences. Chai-1 uses a trunk transformer that processes sequence and structural features without requiring MSA computation, making it faster for single-query predictions. Boltz-2 uses pairwise attention layers similar to AlphaFold 3 but with a more streamlined encoder.

Diffusion Process

AlphaFold 3 conditions its diffusion on Evoformer outputs, separating feature extraction from structure generation into two distinct stages. Chai-1 integrates feature processing and diffusion more tightly in its trunk architecture, with pair representations directly guiding the denoising. Boltz-2 adds a confidence feedback loop that makes the diffusion process adaptive, adjusting its behavior based on predicted quality at each step.

Ligand Handling

All three models accept small molecules as SMILES strings. Chai-1 was specifically optimized for protein-small molecule interactions during training, which gives it strong performance on drug-like compounds. AlphaFold 3 treats small molecules as one of many entity types in its universal framework. Boltz-2 handles ligands through its general molecular representation.

Accuracy Benchmarks

Protein-Ligand Binding Poses

On the PoseBusters benchmark, all three models predict ligand binding poses within 2 angstroms RMSD for approximately 40 to 50 percent of targets. Chai-1 showed particularly strong performance on drug-like molecules at launch, with competitive DockQ scores on protein-ligand targets. AlphaFold 3 demonstrated broad coverage across molecular types. Boltz-2 achieves comparable results on protein-small molecule targets.

Protein-Protein Interfaces

For protein-protein complex prediction, all three models achieve DockQ scores above 0.5 for most heterodimer targets from the CASP15 and CAPRI evaluations. AlphaFold 3 has a slight edge on targets with limited evolutionary information due to its deep MSA processing. Chai-1 performs well on antibody-antigen interfaces, which are critical for therapeutic development. Boltz-2 performs comparably on well-characterized protein families.

Overall Assessment

The accuracy differences between these three models are small enough that they should not be the primary factor in choosing a tool. For most drug discovery applications, all three produce usable binding pose predictions. The practical differences in accessibility, speed, licensing, and API availability matter more for production workflows.

GPU Requirements and Infrastructure

Hardware requirements differ significantly between the three models and represent one of the most important practical considerations:

• Chai-1: requires an A100 80GB GPU. The model's large parameter count and diffusion process memory footprint make it incompatible with smaller GPUs. A single prediction uses 60 to 70 GB of VRAM for typical protein-ligand complexes.
• Boltz-2: runs on A100 GPUs (40GB or 80GB). Its more efficient architecture allows it to fit on smaller GPU memory configurations, making it more accessible for academic labs with limited hardware.
• AlphaFold 3: not deployable by users. All inference runs on Google's internal infrastructure. This eliminates hardware concerns but also eliminates control over compute resources, batching, and throughput.

Speed Comparison

Inference speed varies based on system size (number of residues plus ligand atoms) and the number of diffusion steps:

• Chai-1: approximately 2 to 8 minutes per complex on an A100 80GB, depending on protein size. Larger systems with more than 500 residues take longer due to the quadratic attention scaling.
• Boltz-2: approximately 1 to 5 minutes per complex on an A100. Its confidence-guided diffusion can converge faster for high-confidence targets, sometimes completing in under 2 minutes.
• AlphaFold 3: timing is not publicly disclosed. Users submit jobs through the web interface and receive results via email. The lack of an API makes it unsuitable for any workflow requiring programmatic or batch access.

For virtual screening campaigns that require hundreds or thousands of predictions, the lack of API access for AlphaFold 3 is a significant limitation. Chai-1 and Boltz-2 can both be parallelized across multiple GPUs for high-throughput screening.

Accessibility and Licensing

The accessibility gap between these three models is the most important practical difference:

• AlphaFold 3 is the most restricted. It is only available through the AlphaFold Server web interface with daily prediction limits and a non-commercial license. There is no API, no batch processing, no local deployment, and no ability to integrate with automated pipelines.
• Chai-1 is fully open source with downloadable weights. It can be deployed locally, integrated into pipelines, and used for commercial drug discovery. Chai Discovery also offers a web server for quick predictions.
• Boltz-2 is fully open source with downloadable weights and a permissive license. It can be deployed on any compatible GPU and used without restrictions.

For any production drug discovery workflow, the choice is effectively between Chai-1 and Boltz-2, since AlphaFold 3 cannot be integrated into automated pipelines or used commercially.

Why SciRouter: One API for All Models

Running Chai-1 and Boltz-2 locally requires significant infrastructure: A100 GPUs, CUDA drivers, model weight management, container orchestration, and queue handling for long inference jobs. SciRouter eliminates this complexity by providing both models through a single, unified API.

• No GPU management: both models run on dedicated A100 instances managed by SciRouter
• One API key: access Chai-1, Boltz-2, and 20+ other scientific computing tools with a single key
• Consistent interface: same request/response format for both models, making it easy to compare results
• Async job handling: submit predictions and poll for results without managing queues
• Free tier: 5,000 API calls per month to try both models without commitment

Using Chai-1 via SciRouter API

Here is a complete example predicting a protein-ligand complex using Chai-1:

import requests
import time

API_KEY = "sk-sci-your-api-key"
BASE = "https://api.scirouter.ai/v1"
headers = {"Authorization": f"Bearer {API_KEY}"}

# Predict a BACE1 inhibitor complex with Chai-1
response = requests.post(
    f"{BASE}/complexes/chai1",
    headers=headers,
    json={
        "protein_sequence": "MAQALPWLLLWMGAGVLPAHG...",  # BACE1 sequence
        "ligand_smiles": "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O",  # Ibuprofen
        "num_samples": 5
    }
)
job_id = response.json()["job_id"]

# Poll for results
while True:
    result = requests.get(
        f"{BASE}/complexes/chai1/{job_id}",
        headers=headers
    ).json()
    if result["status"] == "completed":
        print(f"Top pose confidence: {result['confidence']:.3f}")
        print(f"DockQ score: {result['dockq_score']:.3f}")
        # Save the predicted complex structure
        with open("chai1_complex.pdb", "w") as f:
            f.write(result["pdb"])
        break
    elif result["status"] == "failed":
        print(f"Error: {result['error']}")
        break
    time.sleep(15)

Using Boltz-2 via SciRouter API

The same prediction using Boltz-2 follows a nearly identical pattern:

import requests
import time

API_KEY = "sk-sci-your-api-key"
BASE = "https://api.scirouter.ai/v1"
headers = {"Authorization": f"Bearer {API_KEY}"}

# Predict the same complex with Boltz-2
response = requests.post(
    f"{BASE}/proteins/complex",
    headers=headers,
    json={
        "model": "boltz2",
        "chains": [
            {
                "type": "protein",
                "sequence": "MAQALPWLLLWMGAGVLPAHG..."  # BACE1 sequence
            }
        ],
        "ligands": [
            {"smiles": "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O"}  # Ibuprofen
        ]
    }
)
job_id = response.json()["job_id"]

# Poll for results
while True:
    result = requests.get(
        f"{BASE}/proteins/complex/{job_id}",
        headers=headers
    ).json()
    if result["status"] == "completed":
        print(f"Complex confidence: {result['confidence']:.3f}")
        print(f"Interface pTM: {result['interface_ptm']:.3f}")
        with open("boltz2_complex.pdb", "w") as f:
            f.write(result["pdb"])
        break
    elif result["status"] == "failed":
        print(f"Error: {result['error']}")
        break
    time.sleep(10)

Running Both Models for Consensus Scoring

A powerful workflow is to run both Chai-1 and Boltz-2 on the same target and compare results. When both models agree on a binding pose, confidence is higher. When they disagree, it flags targets that need additional investigation.

# After getting results from both models:
chai1_confidence = chai1_result["confidence"]
boltz2_confidence = boltz2_result["confidence"]

# Simple consensus check
if chai1_confidence > 0.7 and boltz2_confidence > 0.7:
    print("High-confidence prediction: both models agree")
elif chai1_confidence > 0.7 or boltz2_confidence > 0.7:
    print("Mixed confidence: review binding poses manually")
else:
    print("Low confidence: target may need experimental validation")

When to Use Which Model

Each model has situations where it is the best choice:

• Use Chai-1 when predicting drug-like protein-ligand complexes, when you need strong small molecule binding pose accuracy, or when working on commercial drug discovery projects
• Use Boltz-2 when you need broader molecular type support (DNA, RNA), when running on a 40GB GPU, when you want confidence-guided predictions, or for large-scale screening campaigns where speed matters
• Use AlphaFold 3 only for non-commercial academic research when you need quick one-off predictions and are comfortable with a web interface and daily limits
• Use both Chai-1 and Boltz-2 for high-value targets where consensus scoring increases confidence in the predicted binding mode

Summary

Chai-1, AlphaFold 3, and Boltz-2 all achieve strong accuracy on protein-ligand complex prediction. The practical differences matter more than benchmark scores: AlphaFold 3 is restricted to non-commercial use with no API access; Chai-1 and Boltz-2 are both open source and API-accessible. Through SciRouter, you can access both open-source models with a single API key and run consensus predictions across models.

Try Chai-1 and Boltz-2 from the SciRouter tools page. For more background, read our introduction to Chai-1 and introduction to Boltz-2.